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In the current period, there has been a prominent and gradual upswing in the application of Internet of Energy (IoE) sensors in smart cities. These sensors play a vital role across diverse aspects of the energy sector, ranging from producing energy to haggling with the complexities of the smart grid. The IoE sensors use fiber optics technology that increases the speed and bandwidth of data transfer in smart grid applications. Incorporating IoE sensors, including fiber optics, is paramount to rehabilitating extensive IoE sensor data into practical information to distribute energy based on prices, availability, and demand for smart homes and electric vehicles. However, from energy generation to consumption, many nodes are incorporated. Therefore, security is a crucial challenge to processing accurate IoE sensor data for energy generation and consumption in smart cities. This paper presents blockchain-enabled, energy-efficient IoE smart grid architecture for smart homes and electric vehicle applications. The proposed architecture suggests blockchain-energy-efficient IoE sensors data scheduling (BEDS) algorithm schemes that consist of blockchain, smart grid, and vehicle and smart home schemes. The paper considers grid data based on sensors for different applications. The proposed system integrates fiber optics, collecting and offloading sensors to the grid for execution. This study aims to process IoE sensor data based on blockchain with a minimum processing time of 29% and less power consumption of 41%. Simulation results show that BEDS has less processing time and energy consumption than existing proof-of-work and proof-of-stake blockchain methods in smart grid networks. • A smart-grid architecture for energy production in smart cities. • Blockchain energy data scheduling (BEDS) algorithm framework. • An energy-efficient blockchain-enabled IoE smart grid architecture. [ABSTRACT FROM AUTHOR]

The world's demand for fossil fuels has recently increased significantly for both transportation and electric power generating sectors. Using these resources not only results in high costs and depletion of them but also increases greenhouse gas emissions and pollution. The electric vehicle (EV) market is growing globally, aiming to face climate change due to greenhouse gas (GHG) emissions and to reduce reliance on fossil fuels. However, the long charging time of EVs and the shortage of charging outlets limits the global adoption of EVs, especially on highways where the problem of accessibility to the electricity distribution grid appears. These issues can be faced by the good planning of charging infrastructure. However, this planning is a multidisciplinary field that includes electricity generation, transportation networks, EVs' characteristics, and driver behavior. A methodology to provide the optimal locations and sizing of electric vehicle charging stations with their own electricity generation and storage using photovoltaic (PV) and energy storage systems on highways considering different factors is proposed in this paper. This paper takes a section of the western desert highway in Egypt connecting Assiut and Cairo cities as a case study. Four scenarios are proposed for the design of EV charging stations' locations and sizing which are centralized charging stations, two-way charging stations, utilizing oil stations' locations, and distributed fixed sizing charging points with a comparison between them. The work also discusses the potential effects of highway slope, wind speed, and number of passengers on the location problem. The results can be used to optimize the design of EV charging stations along highways for a completely sustainable system. [Display omitted] • Full Model of EVs, highway, and traffic flow using Monte Carlo simulation. • Highway EV charging stations optimal allocation methodology using MILP. • Estimating number of chargers for minimizing the waiting time at EV station. • Optimal PV and ESS sizing for standalone EV charging stations. • Different planning scenarios and effects of all parameters are considered. [ABSTRACT FROM AUTHOR]

This paper addresses the integration of electric vehicle (EV) fleets into industrial smart grids to increase operational flexibility. It focuses on an extended multi-objective optimization problem that minimizes two primary objectives: (i) the electricity expenditure of a company using its employees' EV batteries as temporary distributed energy storage, and (ii) the costs associated with the degradation of EV batteries, given the additional usage from the company's perspective. In this paper, the utilization of an EV fleet is simulated at the individual car level over a one-year period. These optimization problems were balanced by using real-time electricity prices and the effective demand response (DR) of the company's electrical load. The company utilized the EVs as battery storage to offset fluctuating electricity prices, while compensating EV owners with free electricity for the costs incurred through degradation of their batteries. The extent to which the company could compensate EV owners while maintaining the viability of vehicle-to-grid (V2G) services in a non-residential scenario was explored. The results established an equilibrium point at which the financial benefits for the company resulting from V2G services was maximized against the negative financial impact of increased battery degradation for EV owners. The results showed that there is a potential mutual benefit between the company and EV owners, even if the company provided EV owners with free charging (based on a percentage of their battery capacity) for each day of their attendance. This mutually beneficial zone ranged from 3%–10% of the battery capacity for AC charging and 6%–17% for DC charging. Optimal Pareto values indicated an economic trade-off that benefited both stakeholders, with DC charging proving significantly more profitable for the company than AC charging (between 257.5% to 38.1% depending on the amount of free charging provided). The findings emphasize the need for an equitable pricing mechanism considering the different characteristics of EVs based on the operational and financial benefits for both parties to create a balanced pricing framework for V2G. • In-depth study on optimization of vehicle-to-grid and electric vehicle battery degradation using MILP. • Mutual benefit for the company and EV owners in an industrial V2G environment. • DC charging was 257.45% to 38.1% more profitable for the company than AC charging. • Comparing the profitability of different EVs in price-based demand response. • Necessity of a fair pricing mechanism for EV owners participating in V2G. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEVs) are gaining market shares due to their ability to employ clean energy, their smooth operation and reduced noise, pollutant emissions and maintenance. Batteries are one of the key technologies in BEV since they strongly affect the vehicle cost and driving range, two of the major concerns of BEV costumers. While the energy storing capabilities of BEVs usually exceed commuting requirements, batteries can be also utilized for home energy management system using bi-directional charging technology. This paper introduces an efficient energy management system for a smart home with BEVs and a bidirectional charger by addressing the corresponding optimal control problem of deciding the battery charging and discharging strategy to minimize energy expenditure and cost. To this aim, the electricity price and expenditure of upcoming weeks is forecasted using data from the present week, and a model of the complete system is used to find the optimal solutions by means of backward induction in a receding horizon approach. The proposed strategy relies on currently available information about the home and vehicle energy expenditure and energy prices in the recent past. The results of the study show a reduction of the electricity cost above 20% in the considered use-case. • Combined use of BEV and smart home for global energy consumption optimization. • Optimization of energy consumption considering hourly cost and power demand. • Backward induction technique embedded in MPC controller for energy optimization. • Reduction in electricity consumption costs by over 20% in a real application. [ABSTRACT FROM AUTHOR]

Smart charging (ability to manage charging processes by time shifting and power control) and bidirectional charging (additional discharging of electric vehicles (EVs)) are essential to decarbonize the power sector. To accelerate EV adoption, use cases should provide a financial benefit for private EV users. We thus evaluate the use cases spot market trading, PV self-consumption optimization, and the combination of both (multi-use) for smart and bidirectional charging for German households using the eFlame model. Our approach involves sequential spot trading on the day ahead, the intraday, and the continuous intraday market. For multi-use, both use cases are applied simultaneously. Key features of this paper are realistic scenarios for today (2021,2022) and 2030, incorporating additional costs, and assessing operational greenhouse gas (GHG) emissions expressed in kgCO2-equivalents. Our results reveal that smart charging is profitable in most cases today and in all cases in 2030, yielding annual electricity cost savings of up to 530 €/EVa compared to direct charging. Bidirectional charging, today profitable only for the combined PV self-consumption and spot market trading use case, becomes universally profitable in 2030, with electricity cost savings ranging from 310 €/EVa to 2780 €/EVa. Influential factors include year-specific price characteristics, user behavior, charging restrictions, and regulations for discharged electricity. Concerning emissions today, some charging strategies reduce operational GHG emissions while others increase emissions. Applying smart and bidirectional charging today is consequently not always beneficial in terms of emissions. In 2030, emissions decrease across all cases, positively influenced by a lower GHG intensity of the German electricity mix and less restrictive constraints on charging and discharging processes. • A novel modeling approach for sequential market trading and combining market trading and PV self-consumption is presented. • Results show that the use case combination yields significantly more electricity cost savings than single use cases. • In 2030, cost savings for smart charging range from 280 –530 €/EVa and for bidirectional charging from 310 –2,780 €/EVa. • Key influences on cost savings are price characteristics, charging hour restrictions, charging power, and user behavior. • Operational emissions today are only reduced in some cases depending on emission intensity and charging losses. [ABSTRACT FROM AUTHOR]

The global rise of electrified transport is bringing significant attention to provision of charging infrastructure and subsequent increases in electricity demand. Whilst much research to date has concentrated on light vehicles these challenges are more extreme for Heavy Goods Electric Vehicles (HGEVs), with power demands exacerbated by larger batteries and the need for rapid turnaround when charging on-route. Colocation with Energy Storage Systems (ESS) could have potential to help, as could intelligent charge control. This paper presents a novel integrated elitist intelligent algorithm that can simultaneously optimise the multiple numerous technical and economic factors needed here, including long term, independent sizing of battery capacity and power-electronic rating, short term ESS management / charger dispatch, and consideration of dynamic electricity price variability. The work goes beyond previous studies by examining the particular challenges of heavy-duty vehicles, considering both charge management of individual vehicles and co-location of static battery storage, and also by contrasting plausible on route and depot-based charging cases. To support this, a method is developed to estimate patterns of HGV attendance at UK fuelling stations, applicable for other countries. Results highlight the economic challenge of on-route charging. Where fleet operation allows idle time at depots, smart control of vehicle charging can track the lowest price electricity time periods. Depot energy delivery cost was seen to reduce from 18.32 to 11.90 p/kWh comparing on-demand and managed charging (based on 2021, UK, half hourly wholesale electricity prices). On-route charging costs can be reduced by the co-location of static ESS but only to 15.74 p/kWh, without consideration of additional commercial costs. All day stations can deliver electricity at a lower average price than daytime only stations and can benefit from comparatively smaller ESS. Cost benefit analysis was applied for a range of assumptions, revealing insight into the non-linear relationship between battery capacity, charger rating, and subsequent energy delivery price. • Assesses the benefit of co-locating ESS with HGEV charging applications. • Intelligent ESS solution addressing long-term sizing & short-term management. • Analyses on-route and depot charging cases with & without separate charge management. • Reflects impacts of recent trends in ESS cost decline & electricity price volatility. [ABSTRACT FROM AUTHOR]

Big data platforms for electric vehicles (EVs) provide data and network advantages for the online battery management system (BMS) algorithms. However, storage costs lead to platform degradation of data quality, which increases the error of online BMS algorithms. Voltage is the raw data for many BMS algorithms, and improving its accuracy and prediction for use in EVs big data platforms has application prospects. In this paper, a high-precision voltage prediction method for the whole life cycle of batteries is proposed. The self-attention network can accurately predict voltage of a sparse dataset by pre-extracting features through transfer learning. A new loss function is proposed to address the problem of overfitting and distortion caused by using sparse data to train the model. Moreover, a training dataset update strategy is proposed to adjust the model according to internal and external factors of battery. The superiority, stability, and robustness of the proposed method are verified by experimental datasets and real-world datasets. The mean-absolute-percentage-error (MAPE) of predicted voltage is less than 0.5%, and it is 1–5% lower than traditional training methods. The proposed method has the advantage of high accuracy, low data requirements, and low computational amount. This study provides the foundation for implementing whole life cycle BMS algorithms. • Sparse voltage data from the new energy vehicle big data platform increases algorithm errors. • A high-precision voltage prediction method for the whole life cycle of batteries is proposed. • An online transfer learning strategy that considers the condition and environment of the battery. • The effectiveness of the proposed method is verified with experimental and real-world datasets. • A fundamental basis for online management algorithms of lithium-ion batteries is provided. [ABSTRACT FROM AUTHOR]

The situation in an urban distribution network becomes intricate under high-impact, low-probability events. Furthermore, the growing quantity of components within the urban distribution network renders their analysis more challenging. Thus, the desired recovery strategy should not only meet the requirements of restoration rates but also guarantee a stable power supply. To address this issue, this paper proposes a multi-objective decision analysis framework, which consists of EV user requirements, MPS routing, and emergency repair crew schedule, to generate a more precise recovery strategy. These objectives' dynamic behaviors in the transportation network are transformed into spatiotemporal characteristic variables and introduced into inequality constraints. Also, the Wasserstein generative adversarial network with gradient penalty is employed in this framework to characterize solar and wind realistic output. By minimizing the weighted sum composed of MPS operational costs, EV user expenses, load loss, and voltage fluctuation, an optimized recovery strategy is provided to the grid operator. Case studies investigate the effectiveness of recovery strategy in IEEE 33-bus and 123-bus benchmark systems. The results indicate the proposed approach can effectively handle complex scenarios achieving rapid and reliable restoration, while also preserving regular charging behavior and maintaining a stable power voltage supply. • Enhance resilience for urban distribution networks under disaster events. • Coordinate multiple recovery components and renewable energy integration. • Consider public requirements for fast restoration, power quality, and EV charging. • Involve renewable energy uncertainty generation. • Identify the strategy selection in response to energy uncertainty. [ABSTRACT FROM AUTHOR]

The surging adoption of electric vehicles (EVs) poses significant challenges for distribution networks (DNs) due to EV charging impact. This paper presents a multi-objective optimization (MOO) model that coordinates EV charging in DNs, aiming to address the interests of different stakeholders, such as the distribution network operator (DNO) and EV owners and achieve a balanced outcome. Specifically, our model's objectives include minimizing the operation costs for the DNO, power loss in the DN, and EV owners' charging expenses, emphasizing the delicate trade-off between these objectives. An innovative improved-augmented epsilon-constrained (I-AUGMENCON) method is proposed to tackle the complex trade-offs by solving the MOO effectively and efficiently. A case study on an modified IEEE 33-bus DN attests to the strategy's efficacy, showcasing a range of solutions that coordinate DNO's costs, DN power loss, and EV charging costs. Furthermore, our I-AUGMENCON outperforms other prevalent MOO solution methods, such as the weighted-sum and Non-dominated Sorting Genetic Algorithm II (NSGA-II), in determining non-dominated solutions and obtaining a Pareto-efficient solution set for the MOO to characterize an effective trade-off between three key objectives. Our model coordinates EV charging to optimize economic and technical objectives, reducing power losses from 6% to around 2% and enhancing voltage stability. By balancing cost savings with power quality, the strategy improves operational efficiency and grid resilience, marking a significant advancement in complex distribution network management. • Enhance multi-objective optimization model clarity for better trade-off visualization in EV charging. • Optimizes interactions between EV owners and DNOs to foster sustainable energy practices. • Improve the AUGMECON method surpassing state-of-the-art methods in efficiency and solution quality. [ABSTRACT FROM AUTHOR]

Highlights • Review of the application of reinforcement learning (RL) for demand response (DR) • DR is relevant for integrating renewable energy source into the smart grid. • Considering RL/DR from energy generation and storage, to supply and user satisfaction. • Typical algorithms and open research directions are discussed. • Most articles focus on single-agent systems in stationary environments. Abstract Buildings account for about 40% of the global energy consumption. Renewable energy resources are one possibility to mitigate the dependence of residential buildings on the electrical grid. However, their integration into the existing grid infrastructure must be done carefully to avoid instability, and guarantee availability and security of supply. Demand response, or demand-side management, improves grid stability by increasing demand flexibility, and shifts peak demand towards periods of peak renewable energy generation by providing consumers with economic incentives. This paper reviews the use of reinforcement learning, a machine learning algorithm, for demand response applications in the smart grid. Reinforcement learning has been utilized to control diverse energy systems such as electric vehicles, heating ventilation and air conditioning (HVAC) systems, smart appliances, or batteries. The future of demand response greatly depends on its ability to prevent consumer discomfort and integrate human feedback into the control loop. Reinforcement learning is a potentially model-free algorithm that can adapt to its environment, as well as to human preferences by directly integrating user feedback into its control logic. Our review shows that, although many papers consider human comfort and satisfaction, most of them focus on single-agent systems with demand-independent electricity prices and a stationary environment. However, when electricity prices are modelled as demand-dependent variables, there is a risk of shifting the peak demand rather than shaving it. We identify a need to further explore reinforcement learning to coordinate multi-agent systems that can participate in demand response programs under demand-dependent electricity prices. Finally, we discuss directions for future research, e.g., quantifying how RL could adapt to changing urban conditions such as building refurbishment and urban or population growth. [ABSTRACT FROM AUTHOR]

Highlights • Geospatial maps are used to estimate the charging load of electric vehicles in cities. • Three distinct charging profiles are assumed in the city: Home, Work, and Other. • Charging stations belong to a mixture of profiles depending on their nearby buildings. • Using 22 kW chargers resulted in a load with a peak of 1.47 kW/electric vehicle. • Fast charging causes high variability in the load when many cars start/stop charging. Abstract In the recent years, the number of electric vehicles (EVs) on the road have been rapidly increasing. Charging this increasing number of EVs is expected to have an impact on the electricity grid especially if high charging powers and opportunistic charging are used. Several models have been proposed to quantify this impact. Multiple papers have observed that the charging stations are used by multiple users during the day. However, this observation was not assumed in any previous model. Moreover, none of the previous models relied on geospatial maps to extract information about the parking lots—where charging stations are installed—and the charging profiles of the potential users of these charging stations. In this paper, a spatial Markov chain model is developed to model the charging load of EVs in cities. The model assumes three distinct charging profiles: Work, Home, and Other. Geospatial maps were used to estimate the charging profile, or mixture of profiles, of the charging stations based on the nearby building types. A case study was made on the city of Uppsala, Sweden—a city with approximately 44,000 cars. The results of the case study indicated that the aggregate load of the EVs in the city reduced the charging impact. For example when using 22 kW chargers, the peak load in the city per EV was estimated to be 1.29 kW/car in case of spatio-temporally opportunistic charging, and 1.47 kW/car in case of residential only opportunistic charging. This is to say that the Swedish grid operators can expect that every EV in the city will increase the peak load by at most 1.47 kW due to aggregation; this is assuming that 22 kW chargers were used. In addition, we showed that the minute-minute variability of the charging load in cities might cause some future challenges. In our case, up to 3% of the EVs in the city simultaneously started charging. This caused a one-minute-ramp in the charging load of 1.1 MW—if charging using 3.7 kW. Charging with higher powers will exacerbate these ramps, e.g., charging with 22 kW will cause sudden one-minute-increases as high as 6.7 MW in the charging load. Such a finding indicates that using high charging powers might cause high variability in the charging load of EVs in cities. This high variability might limit the synergy potentials between EVs and variable renewable energy sources (RES). The proposed model can potentially be used along with RES models to estimate the spatio-temporal synergy potentials between the two technologies. Evaluating the synergy potentials might be of value to grid operators, policy makers, market traders, etc. [ABSTRACT FROM AUTHOR]

Highlights • Shared transportation mitigates traffic and environmental pressures. • Shared transportation is a supplementary way for public transportation. • Shared transportation is gradually affecting the passengers' travel behavior. • Credit management helps to improve shared transportation services. Abstract Electric vehicle sharing (EVS) and bike sharing have been recognized as promising solutions to growingly serious problems of traffic congestion, air pollution, and insufficient parking spaces. This paper, based on the current research status, analyzes and evaluates the effect of shared transportation on alleviating or solving the traffic and environmental issues of mega-cities from five perspectives-resource, environment, convenience, economy and governance. Then, taking Beijing as a case, one of the representative giant cities, this paper investigates and analyzes the development situation, feasibility, and adaptation of shared transportation. Compared with 2015, the share of cars decreased by more than 3.2%, and the trip frequency decreased by more than 55% for private car owners in 2016. Shared transportation reduced energy consumption, nitrogen emissions and PM 2.5 by 45 million liters of gasoline, 540,000 tons and 4.5 billion mg just in one year in Beijing in 2016. The results indicate that shared transportation has an important role and potential in alleviating traffic and environmental issues of mega-cities. A series of development suggestions and some practical considerations are provided at the end, which might supply good decision-making guidance for policy makers. [ABSTRACT FROM AUTHOR]

Highlights • A multi-agent system is developed to simulate the operation of an energy hub with electric vehicles. • Different penetration rates and various charging patterns of electric vehicle are modelled. • A full random dispatch algorithm is integrated in smart charging strategy. • The maximum capacity and potential of vehicle to grid is calculated. Abstract In this paper, a multi-agent system (MAS) was developed to simulate the operation of an energy hub (EH) with different penetration rates (PRs) and various charging patterns of electric vehicle (EV). Three charging patterns, namely uncontrolled charging pattern (UCP), rapid charging pattern (RCP) and smart charging pattern (SCP), together with vehicle to grid (V2G), were simulated in the MAS. The EV penetration rates (EV-PRs), from 10% to 90% with a step of 20%, are considered in this study. Under the UCP, the peak load increases by 3.4–17.1% compared to the case without EVs, which is the reference case in this study. A main part of the increased electricity demand can be supplied by the gas turbine (GT) when the PR is lower, i.e. 71.7% under 10% PR and 37.4% under 50% PR. Under the SCP, the charging load of EVs is shifted to the valley period and thus the energy dispatch of the EH at 07:00–23:00 remain the same as that in the reference case. When V2G is considered, the electricity demand from the grid becomes the largest in all of the cases, e.g. the demand with 50% PR doubles the electricity demand in the reference case. However, the GT output decreases by 2.9–15.7% at 07:00–23:00 due to the effect of V2G. The variations in the EH's operation further raise the changes in energy cost, i.e. the electricity and cooling prices are lowered by 18.3% and 33.8% due to the availability of V2G and the heating and cooling prices increase by 3.5% and 4.3% under the UCP with the PR of 50%. Regarding the V2G capacity, near 39% of the EVs' battery capacity can be discharged via V2G. In addition, the paper also produced a V2G potential line, which is an effective tool to provide the maximum potential of the EVs for peak shaving at any specific time. [ABSTRACT FROM AUTHOR]

The increasing adoption of electric vehicles driven by government plans to strongly electrify mobility will challenge the current distribution grid infrastructure. The related electricity demand increases the risk of overloads and will require significant grid enhancements and infrastructural investments. Our paper evaluates the potential of reducing investment needs through decentralized photovoltaic electricity generation and battery energy storage systems. We use power-flow analyses on representative grid models to test rural, urban, and suburban grids' resilience to higher electric vehicle penetration. We find significant societal benefits with a simultaneous uptake of decentralized generation and estimate savings of up to €3.2 billion solely within the German grid. In rural areas, photovoltaic and battery systems are especially effective for electric vehicle penetrations up to 20%, reducing grid costs by up to 39%. Suburban and urban grids could achieve significant savings for electric vehicle penetrations up to 60%, with cost-saving potentials of up to 51% and 46%, respectively. We recommend that policymakers facilitate decentralized electricity generation to unlock additional benefits from a societal cost perspective, increase the share of sustainable electricity consumption, and improve energy security. [Display omitted] • Estimation of savings potential in EV-related grid cost via PV and battery systems. • Residential generation and storage can significantly reduce grid investments. • Infrastructure cost savings could amount to almost €3.2 billion within the German grid. [ABSTRACT FROM AUTHOR]

The increasing emphasis on clean emission-free transportation has stressed upon the high adoption and penetration of electric vehicles worldwide. However, challenges like range anxiety and long charging time hinder the widespread acceptance. Among various solution the usage of battery swapping station seems more promising as it provide quick battery refueling within a very short time period. The battery swapping station's progress is limited due to the associated investment and operational cost which needs to be addressed to ensure the global acceptance. In this paper, an optimal battery swapping station operation is proposed based on a multi-objective optimization which combines the generation mix of grid, solar PV, and biogas generation along with the battery arrival using mixed integer programming and orderly charging of discharged batteries to allow the swapping station to operate in battery-to-grid mode using time-dependent dynamic programming. The former reduced the cost of charging while the later increases the swapping station revenue. The combined multi-objective optimization increases the daily net profit by almost 20 times as compared to the base case and by 8% in the optimal operation when the batteries are allowed to be discharged in battery-to-grid mode. The results show that the proposed strategy has potential to significantly boost the daily net profit of the swapping station by increasing revenue and cut cost and can contribute towards the acceptance of swapping station in the current EV market. [Display omitted] • Accurate EV arrival and energy forecasts is crucial for station planning. • Use mixed resources like weak grid, solar PV, and bio-gas for energy. • Idle batteries operate in battery-to-grid mode in under served markets. • Model boosts revenue and cuts costs at swapping stations. • Model simulates station modes to boost profit in the EV market. [ABSTRACT FROM AUTHOR]

The decarbonization of the mobility sector motivates to increase the penetration of battery and fuel-cell electric vehicles. The proliferation of these mobility modes will be accompanied by the massive installation of charging and refilling infrastructures into existing networks. This work focuses on fuel-cell vehicles, for which refilling points are needed. Difficulties in hydrogen transportation can be circumvented by deploying onsite hydrogen generation assets (electrolysers) and storage, which can be partially or fully supplied through local renewable generators. Nevertheless, such assets require a considerable initial investment, being necessary the use of planning tools in order to maximize revenues for private investors. This paper focuses on this issue. In particular, an optimal sitting and sizing tool for hydrogen refilling stations with onsite storage and electrolysers is developed. The developed methodology considers the influence of locational marginal prices, which are cleared by the distribution system operator in order to translate the real electricity cost per node. This pricing strategy helps to best allocate assets through the network and thus resulting valuable for planners in order to site refilling infrastructures properly. An original multi-year iterative algorithm based on the multi-cut Benders' decomposition is proposed in order to alleviate the intrinsic high computational cost of the planning tool while accommodate long-term inflation and degradation rates of parameters. A number of simulations are performed on the well-known IEEE 33-bus system. Results verify that locational marginal pricing effectively translates the nodal electricity cost to end-users. Remark, the total electrolysis capacity turns out to be the most significant parameter, reducing further the cost of the project, while storage capacity has a limited influence. Results highlight the importance of the infrastructure capacity when determining the placement and sizing of electrolysers, thus supporting decisions when upgrading existing infrastructure. The impact of the number of stations to be installed and the budget cap is also analysed, showing that both parameters have similar influence and may reduce the total project cost by 70% approximately. The typical scheduling behaviour of the electrolysis-storage facilities is discussed, showing how storage is capable to provide energy arbitrage exploiting locational marginal prices. Finally, the computational performance of the developed algorithm is assessed, verifying that the new tool is efficient and portable. [Display omitted] • A new tool for sitting and sizing refilling stations in distribution networks is developed. • Local marginal prices are considered. • A multi-year iterative algorithm is developed for efficiently solving the problem. • Local marginal pricing translates nodal electricity costs and helps to allocate assets. • The installed electrolysis capacity is the most influence parameter in reducing costs. [ABSTRACT FROM AUTHOR]

The fast-increasing applications of renewable energy and electric vehicles (EVs) pose significant challenges to the safe and economic operation of the power grid. However, through Virtual Power Plant (VPP) technology, EV fleets can be aggregated into large-scale EV generalized energy storage systems, providing scarce dispatchable flexible resources for the future power system. Rapid and accurate prediction of the Aggregated Schedulable Capacity of EV (EVASC) fleets is a crucial technology for VPP with Electric Vehicles (EV-VPP) to participate in various ancillary services of the power system. In this paper, orienting for various dispatch scenarios in the power system, a multi-time scale prediction model of the EVASC for EV-VPPs is presented based on the enhanced Prophet algorithm combining a light gradient boosting machine (LGBM) algorithm. The enhanced Prophet-LGBM algorithm is initially proposed here by incorporating the LGBM algorithm into the traditional Prophet algorithm in series and adding extra sequential features in its input, such as temperature, weather, and alike. In this way, the problem of overfitting and low capability in addressing additional input features in the traditional Prophet is overcome. The proposed enhanced Prophet-LGBM-based prediction method of the EVASC for EV-VPPs is validated using 1.8 million actual charging records of over 4000 charging piles for half a year at the provincial level. The 30-day simulation results of the day ahead and intraday predictions of the EVASC for EV-VPPs show that much higher performances in the prediction accuracy can be achieved, specifically in holidays. • An electric vehicle schedulable capacity forecasting model is presented. • An enhanced Prophet plus a light gradient boosting machine is proposed for the model. • The model can forecast the multi-time scale electric vehicle schedulable capacity. • The model is validated using 1.8 million charging records of 4000 piles • The prediction simulations show much higher accuracy, specifically in holidays. [ABSTRACT FROM AUTHOR]

This study is driven by a company engaged in renewable resource recovery. As the company's battery recycling market continuous continues to grow, the existing recycling facilities are unable to cope with the demand for sorting and storage. As a result, there is an urgent need to identify a suitable electric vehicle battery recycling transfer station (EVBRTS) to alleviate the strain on the recycling plant. The issue of electric vehicle battery recycling location has received scant attention in previous research, with a notable absence of studies focusing on EVBRTS location. Thus, the primary objective of this study is to establish an integrated decision-making framework using the best worst method (BWM) and cumulative prospect theory (CPT) to determine the optimal EVBRTS location. To achieve this goal, a panel of experts representing various sectors was assembled to formulate five primary criteria and fifteen secondary sub-criteria across the economic, social, environmental, technological, and resilience domains, drawing on existing literature and expert insights. The BWM method was employed to determine the criteria weights, followed by a ranking of the alternatives using the CPT approach. Furthermore, an integrated analytical model for the EVBRTS site selection evaluation index system was developed herein. A sensitivity and comparative analysis was carried out to demonstrate the practicality and robustness of the proposed method. A notable finding highlighted in this study is the significant impact that the order in which the expert ratings information was aggregated had on the ranking results. Undoubtedly, this study offers a fresh perspective based on resilience and sustainability for battery recycling managers to make informed strategic decisions on site selection, thereby enhancing resilience in the context of EVBRTS site selection. [Display omitted] • This paper addresses the issue of battery recycling transfer station site. • This study identifies a resilience and sustainability evaluation indicator system. • An analysis framework to locate a battery recycling transfer station is proposed. • Sensitivity and comparison analyses are provided to verify method validity. • Several important theoretical implications and managerial insights are provided. [ABSTRACT FROM AUTHOR]

With the surge of electric vehicles, accurate estimation of their energy consumption becomes increasingly critical. Data-driven models have been widely used for estimating the energy consumption of electric vehicles; however, their applications often face limitations due to inadequate training data, resulting in over-fitting and poor generalizability. In this paper, a physically rational data augmentation approach is proposed to expand the driving trip dataset. By incorporating physical coherence into the augmentation process, new driving trips are synthesized with rational physical context. The effectiveness of the proposed approach is validated by applying it to three data-driven models for estimating the energy consumption of electric vehicles across different validation scenarios. Compared with two baseline data augmentation approaches, our proposed approach demonstrates superior model training performance with less data synthesized. In the best case, the proposed approach achieved a 34% accuracy improvement over the raw data and an 11% improvement over the best-performing baseline. This proposed approach shows considerable promise in facilitating the effective adoption of advanced machine learning algorithms in industrial applications by significantly reducing the data collection requirements. [Display omitted] • The requirements of small-scale data for BEV data-driven models are discussed. • An approach that synthesizes physically rational BEV trip samples is proposed. • Experiments show that conventional data augmentation may produce invalid samples. • The proposed approach effectively improves the performance of data-driven models. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources with power systems has led to greater uncertainties, heightening the need for more flexible services. Demand-side resources can provide significant flexibility cost-effectively, making it crucial to design new business models to harness their flexible capacities. Hence, this paper introduces a three-stage techno-economic framework designed to enhance the exploitation of flexible capacities from demand-side resources such as Internet-of-Things (IoT)-Enabled Appliances (IEAs), Battery-Integrated Rooftop Solar Panels (BIRSPs), and Electric Vehicle (EV) smart charging systems in the local flexibility market. The proposed framework operates in three stages. First, smart homes with IEAs, BIRSPs, and smart charging systems assess their flexibility and report it to their Microgrid (MG) operators. Next, MG operators optimize strategies to maximize market returns and report capacities to the Distribution System Operator (DSO). Finally, the DSO manages flexibility transactions based on MG inputs. A Stackelberg game theory model using a gradient-based method updates the coupling variables between market parties, ensuring convergence in the second and third stages within a decentralized space with limited information sharing. Implemented on a modified IEEE 69-node distribution system, the model fully utilizes the potential of smart homes, including IEAs, BIRSPs, and smart charging systems in the local flexibility market. It reduces the Upstream Network (UN) share in the local flexibility market by 56.86% and decreases DSO costs for energy balancing by 22.36%. • Multi-stage model for releasing IoT-enabled smart home capacities in flexibility markets • Gradient-based Stackelberg game boosts market convergence and limits info sharing • Game theory sets market prices based on all parties' scheduling strategies • Reducing costs by releasing all demand-side capacities into the local flexibility market [ABSTRACT FROM AUTHOR]

Demand response is expected to play a fundamental role in renewable energy communities to alleviate the electricity demand–supply mismatch, especially in the presence of stochastic load and generation. In this paper, we consider an electric vehicle charging station that participates in incentive-based demand response programs. A real-time charging scheme is devised to optimize the charging station operation by coordinating the charging process of the electric vehicles, and complying with the incoming demand response requests. In this context, vehicle demand is assumed uncertain, while demand response requests ask for a change in the charging profile over certain time intervals, in exchange for a monetary reward. By exploiting the probability distributions describing the vehicle charging process, a stochastic formulation is employed to devise a novel charging algorithm aimed at reducing the charging station operational cost. Such a procedure can (i) handle the uncertainty affecting the charging process in different settings and scenarios, and (ii) exploit the information collected in real-time to refine forecasts and hence ensure a higher demand flexibility. Numerical results show that the proposed approach ensures considerable cost reduction compared to the benchmarks, and features highly scalable runtimes. • A stochastic receding horizon scheme is introduced for real-time EV charging. • Acquired online information is employed to refine load forecasts. • EV probability distributions are used to manage the uncertain demand of EVs. • The algorithm performance is validated by comparison with oracle-based optimization. • Computation times are feasible for problem instances involving high EV penetrations. [ABSTRACT FROM AUTHOR]

Highlights • Hybrid electric vehicle technologies emerge due to fossil fuel and global warming impacts. • Different energy management systems for FCHEV have been explained. • FCs with energy storage elements can provide sufficient energy required by FCHEVs. • Optimization techniques are reviewed to improve the performance of future FCHEV. • Issues and challenges are highlighted, recommendations are provided for further FCHEV research. Abstract Hybrid electric vehicle technologies emerge mainly because of the instability in fossil fuel prices, resources and the terrible impact of global warming. As most transport systems use fossil fuel and emit greenhouse gases, many researchers have studied the potential of fuel-cell hybrid electric vehicles (FCHEVs). FCHEVs are vehicles with zero greenhouse gas emission because they only depend on hydrogen. Numerous studies have proven that fuel cells with energy storage elements can provide sufficient energy required by FCHEVs. However, end users demand FCHEVs that are not only efficient in delivering the energy required but can also optimize hydrogen consumption and prolong battery lifetime to compete with current internal combustion engine vehicles. Therefore, advanced optimization algorithms for an FCHEV energy management system (EMS) must be developed to improve the performance efficiency of FCHEVs. This paper presents a critical review of the different types of FCHEV EMSs and their optimization algorithms to solve existing limitations and enhance the performance of future FCHEVs. Consequently, a comprehensive review on the major categories of FCHEV EMSs, such as proportional–integral–derivative controller, operational or state mode, rule-based or fuzzy logic, and equivalent consumption minimization strategies, are explained. This paper also describes optimization techniques such as linear programming, dynamic programming, Pontryagin’s minimum principle, genetic algorithm, particle swarm optimization and rule-based logic optimization for the EMSs of FCHEVs. Furthermore, it focuses on the various factors and challenges of existing optimization algorithms, hydrogen fuel source, environment and safety, and economical and societal concerns, as well as provides recommendations for designing capable and efficient EMSs for FCHEVs. All the highlighted insights of this review will hopefully lead to increasing efforts toward the development of an advanced optimization algorithm for future FCHEV EMSs. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • 1:1 scale laboratory test bench for electric powertrains. • Battery and supercapacitor-based hybrid energy storage systems. • Experimental validation of supercapacitor models. • Novel analytical methods for maximizing the effectiveness of hybrid energy storage systems in real electric powertrains. Abstract This paper deals with hybrid energy storage system (HESS) management strategies, optimized for urban road electric vehicle applications. These new control strategies aim to extend battery pack durability by reducing charging/discharging current peaks by means of supercapacitors. The optimization is carried out with reference to the case study of a HESS composed of a high power unit, i.e. supercapacitor module plus a high energy unit, i.e. a battery pack, based on nickel-chloride ZEBRA (ZEolite Battery Research Africa) technology. On-board integration of the two storage devices is obtained through a DC/DC bidirectional power converter, as this configuration is particularly convenient for many kinds of urban vehicle operations. The novelty of this work consists in an analytical methodology, based on non-linear programming and calculus of variations theory, to evaluate management strategies characterized by high effectiveness in reducing battery current transients. The identification and optimization of these strategies are initially performed on the basis of a vehicle model, built in the Matlab-Simulink simulation environment. To this purpose, the experimental characterization of the supercapacitor module is obtained with reference to three different models, whose selection depends on the required fitting performance and computational effort indexes, as evaluated in the paper. The energy management strategies identified show promising results in the simulation environment, followed by experimental activities carried out by means of a dedicated 1:1 scale laboratory test bench. The various experimental results presented in this manuscript highlight that the identified λ-control strategy presents effectiveness values up to 57%, close to the ideal results obtained in the simulation environment. In fact, the methodology proposed in this paper, validated by laboratory experiments, definitely reduces the negative consequences of power peaks on the HESS, indicating the real possibility of using these results in the design and control of urban road vehicles. [ABSTRACT FROM AUTHOR]

Highlights • Monte Carlo, RBF-NN and Markov-chain are employed for the future passenger amount forecast on a bus. • A model predictive control based air-conditioning system energy management strategy is established for electric buses. • A comprehensive comparison is performed in terms of cabin temperature, energy consumption and computation. Abstract Air-conditioners (AC) usually consume the most electricity among all of the auxiliary components in an electric bus, over 30% of the battery power at maximum. On-board passengers carried by the electric bus are important but random heat sources, which are obsessional disturbances for the cabin temperature control and energy management of the AC system. This paper aims to improve the AC energy efficiency via passenger amount variation analysis and forecast in a model predictive control (MPC) framework. Three forecasting approaches are proposed to realize the passenger amount variation prediction in real-time, namely, stochastic prediction based on Monte Carlo, radial basis function neural network (RBF-NN) prediction, and Markov-chain prediction. A sample passenger number database along a typical bus line in Beijing is built for passenger variation pattern analysis and forecast. A comparative study of the above three prediction approaches with different prediction lengths (bus stops in this case) is conducted, from both the energy consumption and temperature control perspectives. A predictive AC controller is developed, and evaluated by comparing with Dynamic Programming (DP) and a commonly used rule-based control strategy. Simulation results show that all the three forecasting methods integrated within the MPC framework are able to achieve more stable temperature performance. The energy consumptions of MPC with Markov-chain prediction, RBF-NN forecast and Monte Carlo prediction are 6.01%, 5.88% and 5.81% lower than rule-based control, respectively, on the Beijing bus route studied in this paper. [ABSTRACT FROM AUTHOR]

Highlights • A novel PMSM demagnetization and efficiency estimation framework is established. • The efficiency reduction pattern under different demagnetization levels is analyzed. • The impact of demagnetization on the powertrains’ energy efficiencies is evaluated. • A comprehensive comparative study of the energy efficiency is conducted. Abstract Due to possible overheat, abrasion or mechanical vibrations, demagnetization fault is inevitable in permanent magnet synchronous motors (PMSMs), which could greatly decrease the motor’s efficiency and hence an electrified vehicle’s performance. This paper, from an energy efficiency point of view, proposes to analyze the tolerance ability of different electrified vehicles on motor demagnetization faults, via PMSM flux density degradation modeling, efficiency estimation and dynamic programming (DP) based powertrain energy management. The relationship between different demagnetization levels and resultant motor efficiencies is obtained, and analyzed according to the motor operation area. Demagnetized PMSM is adopted in a pure electric vehicle (PEV), a hybrid electric vehicle (HEV) and a plug-in hybrid electric vehicle (PHEV) for energy efficiency analysis. Tolerance analysis indicates that the powertrain efficiency decrease caused by motor demagnetization is more severe under urban driving conditions, especially with PEV and PHEV configurations compared with HEV. A demagnetization threshold investigation is also given in this paper. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • 1:1 scale laboratory test bench for electric power-trains supplied by hybrid energy storage systems. • ZEBRA and super-capacitors based hybrid energy storage systems. • Rule based energy management strategies. • Laboratory experimental data on standard driving cycles. Abstract This paper is aimed to experimentally analyse the effectiveness of a hybrid storage system, when powering a commercial vehicle for urban use. The hybrid energy storage system is composed by two ZEBRA batteries, combined with an electric double layer capacitor (EDLC) module. The integration of those storage systems is obtained by means of a bidirectional DC/DC converter, which balances the electric power fluxes between batteries and super-capacitors, depending on the driving operative conditions. Modeling and simulations are preliminarily conducted with reference to the specific case study of an electric version of the Renault Master, supplied by the above described hybrid storage system. That theoretical activity allows the optimization of rule based energy management strategies for the hybrid energy storage system, in terms of the effectiveness in reducing the negative effects of high charging/discharging currents on battery durability. Then, the experimentation of the real power train, connected to the mentioned hybrid storage system, is carried out through a 1:1 laboratory test bench, able to perform the analysed energy management strategies on standard driving cycles, representative of the urban mission of the commercial vehicle under study. The obtained experimental results, expressed through electrical and mechanical parameters in a wide range of road operative conditions, show that the super-capacitors can improve the expected battery lifespan, with values of maximum effectiveness up to 52%, for driving patterns without negative road slopes. The procedure followed and presented in this paper definitely demonstrates the good performance of the evaluated hybrid storage system, controlled by the DC/DC power converter, to reduce the negative consequences of the power peaks associated with the urban use of commercial vehicles. [ABSTRACT FROM AUTHOR]

Development of electric mobility and sustainable energy result in new technologies such as contactless electric vehicle charging and roadway energy harvesting methods, but also self-healing asphalt roads. By combining these technologies a new concept of Future Sustainable Roads for Electric Mobility is created and presented in the paper. This paper bridges the gap created by these unilateral technology developments using a multi-disciplinary approach including placing cautions when necessary and suggesting viable alternatives for optimal utilization of these energy transfer and conversion techniques. Through theoretical analysis, simulations, and tests on lab-scale experimental prototypes, the impact of our proposal is showcased. Thermal and loss models are developed for self-healing asphalt. Also, integration study of solar roads and contactless charging is performed. Applying the insight gained from the results, it is discussed how some challenges also pave a way towards interesting opportunities, for instance, infrastructure sharing for material use optimization and efficient mosaic integration. Finally, an economic viability case study is presented for a future Dutch highway with such newly emerging components. [ABSTRACT FROM AUTHOR]

The Los Angeles Air Force Base Electric Vehicle Demonstration is a currently ongoing vehicle-to-grid demonstration project with the objective of minimizing the cost of operation of a fleet of approximately 30 electric vehicles (EVs) through participation in the California Independent System Operator (CAISO) frequency regulation market. To accomplish this, a hierarchical control system has been developed to optimize, plan, and control the charging, market bidding, and response to grid system operator control of the EVs. This paper presents an overview of the day-ahead optimization model component of the hierarchy. The model is a mixed integer linear program that optimizes daily EV charging and regulation capacity bids strategies in order to minimize operation costs and maximize ancillary service revenue. A deterministic approach is used due to several practical concerns of the demonstration project, including model complexity and the availability and uncertainty of input data in day-ahead decision making, and the limited size of the fleet. The model includes additional user-defined parameters to tune model behavior to better match real-world conditions and minimize the risks of uncertainty. The paper conducts scenario analysis to explore the impact of these parameters on high level model behavior and resulting bid strategy. The parameters explored include hourly regulation prices, local load conditions leading to retail demand charges, forced symmetry constraints for regulation bids, SOC penalty values to reserve higher states-of-charge in vehicles, and expected regulation resource utilization while providing reserves. These analyses show significant sensitivity in the frequency regulation bidding strategy to the regulation utilization, as well as large differences in the regulation prices between regulation up (discharging capacity) and regulation down (charging capacity). Results also suggest enforcing symmetry in regulation appears to have significant impacts in regulation revenue when there is large relative disparities between prices in the up and down direction. Finally, imposing a small cost on low SOC values significantly impacts the fleet-wide average SOC, making the system more resilient to uncertainty in the mobility demands gathered at the time of making day ahead decisions. [ABSTRACT FROM AUTHOR]

The growing deployment of new flexible resources, renewable energy resources (RES), and Electric Vehicles (EV) in the distribution system necessitates new methods to manage the distribution system operation optimally. In this regard, our paper, by deploying the concept of Virtual Power Plants (VPPs) as the aggregation of multiple agents and local power markets that are known as important tools for future power systems presents a management framework for the distribution systems with high penetration of EVs. To this end, the interaction of the DSO and VPPs is studied based on their strategic behaviour through the local active and reactive power markets. This way, a bilevel optimization approach is proposed where the DSO aims to minimize its operational cost by setting the operation point of its own facilities and determining the hourly active and reactive power prices for VPPs considering the distribution system congestion in the upper level. At the lower level, VPPs try to minimize their cost by scheduling their assets based on the local active and reactive power prices set by the DSO. The results show how nodal pricing in local markets could improve the distribution system operation. In addition, it is indicated that Reactive Power Support (RPS) from VPP-owned EVPLs can decrease the VPPs' cost by gaining profit in the reactive power market and facilitating their participation in the active power market. • Distribution system management considering the strategic behaviour of the VPPs. • Bilevel optimization for local active and reactive market. • Optimal dispatching strategy of the DSO and VPPs considering EV parking lot. • Assessing the potential of EV parking lot to provide reactive power and flexibility. • Managing the challenges of high EV deployment and assessing their potential benefits. [ABSTRACT FROM AUTHOR]

The electrification of the transportation sector poses an opportunity for reducing greenhouse gas (GHG) emissions from passenger vehicles. Electric vehicle (EV) charging through dynamic wireless power transfer (DWPT), known as roadway electrification, could shift EV demand profiles to better coincide with renewable electricity generation. However, this would be a very large new load and few studies evaluate the regional impacts of DWPT charging in a power transmission system. This paper defines methods that address dataset generation for passenger vehicle trips and models to evaluate regional impacts for this emerging technology. Household vehicle miles traveled (VMT) data form localized EV demand profiles through discrete-event simulation. This data serves as exogenous inputs for a Production Cost Model (PCM) of a synthetic transmission system based on the Electric Reliability Council of Texas's (ERCOT) network. EV charging methods are compared for both a 2018 baseline generation mixture and a high-renewable generation case incorporating 20 GW of installed solar photovoltaic (PV) capacity. The PCM employs unit commitment and economic dispatch (UC&ED) models to compare financial, environmental, and grid reliability impacts from EV charging across passenger EV adoption levels. In-transit charging could reduce grid operational costs by as much as 1.49%, with up to $13.7B saved in annual vehicle operational costs for consumers compared to gas-powered vehicles. Health impacts analysis from power plant and vehicle tailpipe emissions from this study show net health benefits increase by 40% for in-transit charging coupled with high renewable generation. Renewable resources provide an avenue for cost-effective in-transit charging with reduced emissions. The combination of dataset generation and open-source power system modeling establish a foundation for the holistic evaluation of regional DWPT impacts. • PEV dynamic charging with increased renewable generation has positive system impacts. • Public data informs vehicle-miles traveled datasets for energy consumption analysis. • In-transit wireless charging from PEVs decreases power system peak demand hours. • In-transit PEV charging aligns with solar power to reduce curtailment and total cost. • Net health benefits from PEVs improve with in-transit charging and renewable sources. [ABSTRACT FROM AUTHOR]

Current lithium-ion battery (LIB) management technique relying solely on the limited time-domain measurements appears to reach its limit, and incorporating new sensing information, particularly the impedance of LIB, offers a promising path for improvement. Using the non-stationary random driving profile of electric vehicle (EV), a method to extract online passive electrochemical impedance spectroscopy (OPEIS) is proposed and validated, with relevant factors influencing its efficacy also investigated. The particularity of actual driving profile is revealed both theoretically and experimentally, and beyond expectation, the highly differentiated driving profiles yield a similar spectral pattern, which facilitates the acquisition of OPEIS. Continuous OPEIS characterized by the distribution of relaxation time (DRT) ranging from 0.2 Hz to 3 kHz are analyzed in detail under different battery conditions. Compared with offline reference EIS, most of the measurement errors are <3%. Based on the acquired spectral information and OPEIS, a safety-relevant detector and an internal temperature estimator for LIB are presented, and they can both realize a near instant electrochemical sensing up to 1 Hz. As the underlying opportunities of OPEIS are outlined, challenges in its reliable acquisition and engineering implementation are evaluated as well. By comprehensively discussing the realization of OPEIS, research in this paper is expected to provide valuable references for a more effective battery management. • A straightforward OPEIS acquisition and preprocessing strategy is presented. • Spectral features of the driving profile in day-to-day use of EV are revealed. • The feasibility and performance of OPEIS are evaluated from multiple dimensions. • The positive role of OPEIS in battery management and the challenges in accessing it are outlined. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) possess untapped potential as mobile power banks for actively delivering electricity between different energy communities, known as Community-to-Vehicle-to-Community (C2V2C) service. While C2V2C represents an effective means of inter-community electricity sharing, limited research explores EVs' role in electricity delivery between locations. Suitable control approaches of EV charging for the C2V2C service are lacking, and it is unclear how robust the C2V2C service is and how its performance is affected by different factors. This paper aims to bridge these research gaps by developing an advanced control of EV smart charging/discharging to facilitate the C2V2C service. By comparing the power balance in the EVs' current-connecting and next-destination communities, the advanced control derives a target state-of-charge for the EVs in the current-connecting community, which can optimize the electricity delivery between the two communities. Then, the robustness of the C2V2C service is analyzed by evaluating its performances under different scenarios. Major factors like community combinations, renewable energy system (RES) configurations, EV battery capacity and numbers are examined for their impacts on C2V2C performance. The findings demonstrate that the C2V2C service significantly enhances energy balance across diverse community combinations, particularly in workplaces with substantial RES capacity. A large EV battery capacity is beneficial for performance improvements, but the impact diminishes at higher values due to limited surplus renewables availability. The increasing EV number enhances both electricity delivery capability and utilization of self-produced renewables. This study validated the effectiveness of the C2V2C service and provides valuable insights into optimizing its application across different scenarios. • Propose Community-to-Vehicle-to-Community (C2V2C) for inter-community energy sharing. • Study mechanisms of electricity delivery and sharing via EV over-charging/discharging. • Develop an advanced control to optimize EVs' electricity delivery and sharing. • Analyze robustness of the C2V2C service under different scenarios. • Reveal how building-/RES-/EV-related factors affect the C2V2C service performance. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) and their charging infrastructure have witnessed rapid adoption into the smart grid owing to their ability to reduce the transportation sector's emissions. However, this hasty deployment has left the grid prone to attacks initiated through the charging ecosystem. This paper develops a family of robust dynamic load-altering attacks that manipulate the EV load to destabilize the power grid. The attack strategy is formulated based on feedback control theory and Linear Matrix Inequalities (LMIs) and incorporates mathematical modeling of the power grid uncertainties. The effectiveness of our proposed robust EV attack strategy is demonstrated through extensive simulations of attack scenarios against the New England (NE) 39-bus grid. The results demonstrate that the proposed attacks were able to destabilize the grid frequency inducing a deviation of 1.5 Hz and forcing the generators to trip after compromising as little as 6.6% of the total grid load. We also present a two-tier Deep Learning (DL) attack detection mechanism. We optimize this mechanism to achieve optimal results against dynamic and switching attacks. The proposed mechanism was evaluated against several other models and proved to be superior with an accuracy of 99.9%. • We propose a robust EV Dynamic Attack Design that takes advantage of compromised EVs to cause power grid instability. • The controller design accounts for several uncertainties that can impact the attack success. • We perform extensive time domain simulations to demonstrate the devastating impact of such attacks even in the presence of uncertainties. • The proposed scheme demonstrates its ability to cause generator tripping even with just 6.7% of the total grid load. • We then propose a two-tier detection mechanism that achieved a detection accuracy above 99.9%. [ABSTRACT FROM AUTHOR]

Accurate and rapid fault detection is essential for the safe operation of lithium-ion batteries in electric vehicles. However, conventional fault detection methods dependent on constant thresholds may have false alarms or missing alarms due to the inevitable disturbances resulted from the battery system modeling errors and measurement noises. In this paper, we design a multi-objective nonlinear fault detection observer for lithium-ion batteries, which is robust against disturbances but sensitive to battery multi-fault. We then perform formal stability and L ∞ / H _ performance analysis for the resultant estimation error system. Furthermore, tractable design procedures for the observer gain parameter and an adaptive threshold are derived. Then, via adaptive thresholding, a delicate three-step multi-fault detection scheme is developed to detect the occurrence of battery various faults, including short-circuit faults, current and voltage sensor faults. Finally, the efficacy of the proposed scheme is validated under several experimental case studies involving a variety of faults with their different levels of severity and erroneous SOC initialization. • A general battery model that incorporates both multi-faults and disturbances. • A multi-objective nonlinear observer with formal stability and performance analysis. • A three-step multi-fault detection scheme with an adaptive threshold. • Extensive experimental studies involving a variety of faults. [ABSTRACT FROM AUTHOR]

This paper presents a new prosumer-centric peer-to-peer (P2P) energy trading framework incorporating the network-secure export–import limits, known as dynamic operating envelope (DOE), for prosumers considering their preferences to trade in the local market. In the proposed framework, a DOE-integrated energy management algorithm is developed for prosumers considering electric vehicles (EVs) charge–discharge and photovoltaic (PV) systems. The framework allows prosumers to operate within defined DOE, aligning with the preferences of EV-owning prosumers by managing the desired EV battery charge levels at their departure. An auction-based P2P energy trading mechanism for prosumers is proposed incorporating the allocated DOE to ensure secure network operation and valuing prosumers' preferences to benefit them. The proposed framework is implemented on a 78-node low-voltage (LV) residential distribution network to evaluate its performance. The results and comparative analyses verify the effectiveness of the proposed framework in addressing network issues such as voltage and line congestion problems, meeting prosumers' preferences in asset management, and providing benefits to all participants in the energy trading process. • A prosumer-centric P2P energy trading using network-secure export–import limits. • Hybrid DER marketplace model-based framework consisting of the DSO and the DMO. • Energy management within the limits considering EVs charge–discharge and PV systems. • DOE-integrated Double auction-based market mechanism for P2P energy. • Fairness in allocating DOEs with high RPI, PIEI, QoS, QoE and MiM values. [ABSTRACT FROM AUTHOR]

There are several advantages in keeping motor drive DC input voltage stable around its nominal value especially when it comes to minimize losses. This paper deals with the stability enhancement of the motor drive DC input voltage of an electric vehicle with on-board hybrid energy storage system. On one hand, the natural voltage variation at the output battery pack can be avoided by using a DC/DC converter connected between the battery and the motor drive. On the other hand, the incorporation of supercapacitors in small urban electric vehicles enables handling power peaks thus reducing the battery current root-mean-square value, which in turn increases the battery lifetime. Several topologies can be considered for the coupling of supercapacitors with the vehicle energy system. In this paper, three topologies are studied: battery-only, direct hybrid coupling, and active parallel hybrid coupling of batteries and supercapacitors. A reduced-scale power level hardware-in-the-loop test-bench has been built to analyze the performance of the hybrid topologies under the ARTEMIS driving cycle. Experimental results show the effectiveness of the active parallel configuration controlled by an improved energy management strategy that dynamically regulates the supercapacitors state-of-charge. The analysis performed demonstrates that the use of this configuration coupled with an improved management strategy can increase the power transferred by 80% compared with a battery-only configuration, and by 40% compared with direct hybrid coupling or active parallel coupling configuration with a traditional management strategy, keeping the voltage stability of the DC Link. [ABSTRACT FROM AUTHOR]

The accuracy of identifying the parameters of models describing lithium ion batteries (LIBs) in typical battery management system (BMS) applications is critical to the estimation of key states such as the state of charge (SoC) and state of health (SoH). In applications such as electric vehicles (EVs) where LIBs are subjected to highly demanding cycles of operation and varying environmental conditions leading to non-trivial interactions of ageing stress factors, this identification is more challenging. This paper proposes an algorithm that directly estimates the parameters of a nonlinear battery model from measured input and output data in the continuous time-domain. The simplified refined instrumental variable method is extended to estimate the parameters of a Wiener model where there is no requirement for the nonlinear function to be invertible. To account for nonlinear battery dynamics, in this paper, the typical linear equivalent circuit model (ECM) is enhanced by a block-oriented Wiener configuration where the nonlinear memoryless block following the typical ECM is defined to be a sigmoid static nonlinearity. The nonlinear Weiner model is reformulated in the form of a multi-input, single-output linear model. This linear form allows the parameters of the nonlinear model to be estimated using any linear estimator such as the well-established least squares (LS) algorithm. In this paper, the recursive least square (RLS) method is adopted for online parameter estimation. The approach was validated on experimental data measured from an 18650-type Graphite/Lithium-Nickel-Cobalt-Aluminium-Oxide (C 6 /LiNiCoAlO 2 ) lithium-ion cell. A comparison between the results obtained by the proposed method and by nonparametric frequency-based approaches for obtaining the model parameters is presented. It is shown that although both approaches give similar estimates, the advantages of the proposed method are (i) the simplicity by which the algorithm can be employed on-line for updating nonlinear equivalent circuit model (NL-ECM) parameters and (ii) the improved convergence efficiency of the on-line estimation. [ABSTRACT FROM AUTHOR]

As a main driving force of electric vehicles (EVs), the losses of in-wheel permanent-magnet synchronous motor (PMSM) direct drive system can seriously affect the energy consumption of EVs. This paper proposes a loss optimization control strategy for in-wheel PMSM direct drive system of EVs which optimizes the losses of both the PMSM and the inverter. The proposed method adjusts the copper losses and iron losses by identifying the optimal flux-weakening current, which results in the PMSM achieving the lower losses in the whole operational range. Moreover there are strongly nonlinear characteristics for the power devices, this paper creates a nonlinear loss model for three-phase half-bridge inverters to obtain accurate inverter losses under space vector pulse width modulation (SVPWM). Based on the inverter loss model and double Fourier integral analysis theory, the PWM frequency is optimized by the control strategy in order to maximize the inverter efficiency without affecting the operational stability of the drive. The proposed loss optimization control strategy can quickly find the optimum flux-weakening current and PWM frequency, and as a result, significantly broaden the high efficiency area of the PMSM direct drive system. The effects of the aforementioned strategy are verified by both theoretical analysis and experimental results. [ABSTRACT FROM AUTHOR]

In this paper, the performance of both silicon carbide (SiC) MOSFETs and silicon (Si) IGBTs based electric vehicle (EV) traction systems are investigated and compared comprehensively, particularly from the efficiency point of view. Both conduction loss and switching loss of SiC-MOSFETs are analyzed and modeled taking temperature effect into account. Such approach yields a more accurate prediction of SiC losses. The temperature distribution of SiC-Inverter is described by ANSYS finite element analysis (FEA), and compared with Si counterparts. According to the lower losses and higher thermal conductivity, SiC exhibits much lower temperature than Si under the same power rating and cooling condition. Subsequently, this paper goes further by conducting an investigation of the effect of SiC-Inverter on the motor efficiency. Experimental results show that the SiC-based inverter-motor traction system has observably higher efficiency of overall system compared to the Si-based traction system, and first explore that the motor has extremely high efficiency under low speed and light load when it is driven by a SiC-MOSFETs based inverter due to the higher switching speed of SiC MOSFETs. Meanwhile, the experimental results also confirm the losses models of SiC MOSFETs. [ABSTRACT FROM AUTHOR]

System inertia reduction, driven by the integration of renewables, imposes significant challenges on the primary frequency control. Electrification of road transport not only reduces carbon emission by shifting from fossil fuel consumption to cleaner electricity consumption, but also potentially provide flexibility to facilitate the integration of renewables, such as supporting primary frequency control. In this context, this paper develops a techno-economic evaluation framework to quantify the challenges on primary frequency control and assess the benefits of EVs in providing primary frequency response. A simplified GB power system dynamic model is used to analyze the impact of declining system inertia on the primary frequency control and the technical potential of primary frequency response provision from EVs. Furthermore, an advanced stochastic system scheduling tool with explicitly modeling of inertia reduction effect is applied to assess the cost and emission driven by primary frequency control as well as the benefits of EVs in providing primary frequency response under two representative GB 2030 system scenarios. This paper also identifies the synergy between PFR provision from EVs and “smart charging” strategy as well as the impact of synthetic inertia from wind turbines. [ABSTRACT FROM AUTHOR]

Fleets of commercial vehicles for delivery services in urban areas constitute road transportation means which are required to run relatively short distances and to respect anti-pollution laws commonly imposed by many municipalities. For this kind of commercial applications, high efficiency and eco-friendly electric propulsion systems offer an interesting alternative to thermal engines. This paper is focused on the analysis of such solution, by presenting experimental results obtained with a ZEBRA battery based propulsion system, designed to power a specific urban unit within the category of electric commercial vehicles. A novel contribution is added to the relevant literature concerning battery based electric powertrains for road vehicles. The main novelty consists in a wide range of experimental results and performance analysis carried out with reference to the real behavior of both the whole propulsion system and each main component, when powering the commercial vehicle, on the urban part of the NEDC (New European Driving Cycle) standard driving cycle, at different slopes. The experimental results, expressed through electrical and mechanical parameters, are initially evaluated by means of a quasi-static numerical model of the electric powertrain and then experimentally verified with the support of a 1:1 scale laboratory dynamic test bench. The procedure followed and presented in this paper definitely demonstrates the good design and performance, obtained for the evaluated propulsion system, in satisfying the real energy and power requirements, specific of an urban use for delivery commercial vehicles, in terms of daily autonomy and slopes. The collections of experimental results, analyzed in the paper, represent in addition a useful set of data for simulation in order to build, verify and improve models in their outputs. [ABSTRACT FROM AUTHOR]

• The proposed model presents an integrated modelling framework in day-ahead and real-time markets. • The solution method compromises two-stage optimization processes for the optimization horizons. • The impact of traffic in routes is modelled in the multi-stage multi-level optimization process. • The sectionalizing process of the distribution system is modelled. This paper presents an integrated framework for the optimal resilient scheduling of an active distribution system in the day-ahead and real-time markets considering aggregators, parking lots, distributed energy resources, and Plug-in Hybrid Electric Vehicles (PHEVs) interactions. The main contribution of this paper is that the impacts of traffic patterns on the available dispatchable active power of PHEVs in day-ahead and real-time markets are explored. A two stage framework is considered. Each stage consists of a four-level optimization procedure that optimizes the scheduling problems of PHEVs, parking lots and distributed energy resources, aggregators, and active distribution system. The distribution system procures ramp-up and ramp-down services for the upward electricity market in a real-time horizon. The active distribution system can utilize a switching procedure to sectionalize its system into a multi-microgrid system to mitigate the impacts of external shocks. The model was assessed by the 123-bus test system. The proposed algorithm reduced the interruption and operating costs of the 123-bus test system by about 94.56% for the worst-case external shock. Further, the traffic pattern decreased the available ramp-up and ramp-down of parking lots by about 58.61% concerning the no-traffic case. [ABSTRACT FROM AUTHOR]

Power battery packs are the energy source of battery electric vehicles (BEVs). A precise state-of-health (SOH) estimation for batteries is crucial to ensure the operational security and stability of BEVs. This paper employs an equivalent circuit model of battery pack in SOH estimation. Since a battery pack is a complex and nonlinear system, the equivalent circuit model of battery pack is always complicated. To balance estimation accuracy and computational complexity, the equivalent circuit model of battery pack should be simplified. However, much noise is produced in the simplified model. In addition, the errors during SOH estimation are from various sources so that SOH estimation is a non-Gaussian problem. Given the genetic resampling particle filter (GPF) performs efficiently in solving non-Gaussian problems, this paper proposes a new GPF-based method for battery SOH dynamic estimation when accuracy of the equivalent circuit model is not high. First, a second-order equivalent circuit model of Resistance–Capacitance (RC) circuit for the battery pack is developed. The unknown parameters are identified using the recursive least-squares method with forgetting factor. Second, a state-space model of the GPF is developed based on the equivalent circuit model. Finally, a case study is conducted using real data collected from operating electric taxis in Beijing to investigate the estimation performance of the proposed model. Estimation results show that the proposed GPF model outperforms the particle filter method in the SOH estimation problem. [ABSTRACT FROM AUTHOR]

As electric vehicles (EVs) rapidly proliferate, they intensify the demands on coupled power and transportation networks (CPTNs), leading to operational challenges such as congestion and overload. To address these challenges and enhance the dynamic performance of CPTNs, this paper proposes a coordinated expansion planning model based on a dynamic modeling method to support the upgrading of CPTNs (in distribution lines, distributed generators, chargers and roads). The spatial–temporal evolution and interaction of power flow in power distribution systems and traffic flow in transportation networks is incorporated into a dynamic network equilibrium, revealing the impact of network expansion on system performance. Uniquely, it replaces static link models with a novel point queue model to better track queuing and charging dynamics at fast charging stations. A scenario-based bi-level stochastic optimization model is formulated to determine the optimal coordinated expansion strategies, considering the diversity of electric and traffic demand scenarios. The optimization problem is solved using a designed marginal-cost-based particle swarm optimization algorithm. Case studies demonstrate the model's effectiveness in alleviating 95.89% traffic congestion and reducing 4.75% low voltage risk and 28.86% overload risk, marking a significant advancement in CPTN management. • A novel point queue model is introduced to track the dynamic charging process in FCSs. • A dynamic network equilibrium is established to capture the power and traffic flow. • Uncertainties in electric and traffic demand are addressed by stochastic optimization. • Bottleneck facilities causing network congestion and operating risk can be identified. • The proposed expansion strategy serves to enhance the dynamic performance of CPTNs. [ABSTRACT FROM AUTHOR]

This paper examines the economic and policy implications of the charging, network and auxiliary infrastructure required for a fully electrified metropolitan Melbourne bus network using overnight depot charging. We introduce the concept of minimum and maximum fleet charging capacities, as well as a charging infrastructure augmented total cost of ownership that is faced by bus fleet owners. Our analysis provides policy makers with a range of viable fleet charging capacities and a cost component breakdown of public bus fleet electrification using battery electric vehicles. We find that a minimum of half the fleet's rated charging capacity is needed to maintain operation, which provides a lower bound for network and charging infrastructure costs. Considering the augmented total cost of ownership (per km), charging, network and auxiliary infrastructure costs contribute 10–19%. A gradual transition to battery electric buses over 12 years yields further TCO savings of between 4.6 and 5%. We recommend streamlining the network augmentation process to reduce uncertainty and project delays. • Calculation of viable charging capacities to operate a metro BEV bus fleet • BEV buses operate using an overnight charging strategy with real world schedules • Fleet can continue operating with half its rated charging capacity • TCO is augmented with charging and network infrastructure costs • Charging, network and auxiliary infrastructure contributes 10–19% to the TCO [ABSTRACT FROM AUTHOR]

As the significance of participation in the Day-Ahead Market (DAM) for stakeholders managing the charging of Electric Vehicle (EV) fleets increases, the necessity for precise EV Load Curve (EVLC) forecasting emerges as crucial. This paper presents an extensive investigation of nine diverse EVLC forecasting methodologies, encompassing statistical, machine learning, and deep learning techniques. These methodologies are evaluated on four public, real-world EV datasets, keeping in line with the specific forecasting horizon required for DAM. The study incorporates models with and without online historical data, ensuring broad applicability across varied data availability scenarios. An exploration of seasonal variations in forecasting performance is conducted via one year-long rolling simulations, providing deep insight on seasonal patterns. Furthermore, a detailed methodology for constructing EVLCs from session-based, tabular EV datasets is presented. The conducted research establishes a first-of-its-kind comprehensive comparison of EVLC forecasting methodologies for the real-world challenge of DAM participation. The research results provide indispensable guidance to wholesale electricity market participants, namely suppliers and electric vehicle aggregators, advancing the understanding in the field and significantly contributing to optimization of DAM participation for EV fleets. • Evaluation of forecasting in specific horizon required for DAM participation. • Nine diverse EV Load Curves forecasting methods. • Forecasts are evaluated on four public, real-world EV datasets. • Evaluation for a year-long period and diverse characteristics of EV charging data. • Methodology for constructing EV Load Curve from session-based tabular EV dataset. [ABSTRACT FROM AUTHOR]

With the popularity of electric vehicles (EVs) in urban areas, the effective utilization of their charging flexibility in active distribution networks (ADNs) has drawn wide attentions. This paper proposes a bilevel model for the collaborative operation of ADNs with multiple charging stations (CSs) considering EV drivers' willingness. In the upper level, the distribution system operator (DSO) minimizes the total operational cost of ADNs and sets the optimal energy and reserve prices to trade with CSs under loads and market uncertainties. In the lower level, the aggregated model of EVs including drivers' response willingness is firstly established. Then, CSs minimize their own costs by adjusting bidding quantities and setting optimal incentive prices for EV drivers. Further, the developed bilevel model considering responsivities of EV drivers and various uncertainties is transformed into a tractable single level problem via the deterministic reformulation, Karush-Kuhn–Tucker (KKT) conditions and linearization method. Finally, numerical studies show that the proposed method can facilitate the DSO, CSs, EV drivers to reduce 3.7%, 20%, 9.6% total costs on average and improve the network security of ADNs under various uncertainties. Moreover, the scalability of the proposed approach is verified in the IEEE 123-bus DN with the computation time less than 600 s, which satisfies the computational efficiency requirement for the ADN day-ahead optimal scheduling. • A bi-layer approach is proposed for the optimal coordinated operation. • The EV drivers' willingness to participate in DR program is modeled and embedded. • The tractable reformulation of the proposed bilevel model is developed. • The scheduling potentials of EVs are verified. • The effectiveness, stability and scalability of the proposed model are validated. [ABSTRACT FROM AUTHOR]

This paper proposes an aggregated flexibility estimation method considering the distributed electricity-hydrogen (H 2) interactions for virtual power plants (VPPs) to enhance the economic benefits from the peak-regulation market (PRM) while facilitating the accommodation of renewable generation. Firstly, various distributed energy resources (DERs) such as electric vehicles (EVs), air conditioning systems (ACs), and electricity-H 2 coupled hydrogen refueling stations (HRSs) are modeled as virtual storage (VS) with characterized charging/discharging response parameters. Besides, the temporal coupling power profile of VS can be abstracted as a polytope from the geometric perspective, and the aggregated high-dimensional flexibility region of VPP is formulated by the Minkowski sum for these polytopes. Furthermore, a day-ahead peak-regulation optimal scheduling model considering the conditional value at risk (CVaR) is developed for the electricity-H 2 integrated VPP participating the PRM, and the dimensionality of the aggregated polytope is reduced by the inscribed hyperbox approximation to provide a compact and concise scheduling region. Comparative studies have validated the aggregated power range and net income can be improved by 13.05% and 15.11% with the mechanism of electricity and hydrogen integrations. • The aggregated flexibility of distributed electricity and H 2 resources is unlocked. • Different types of virtual storage models are designed for EVs, ACs, and HRSs. • An inscribed hyperbox based aggregated flexibility projection method is proposed. • A concise day-ahead peak-regulation optimal scheduling model is developed for VPPs. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage framework to facilitate the integration of distributed energy resources (DERs) within emerging distribution-level markets. Market prices significantly influence the decision-making process for prosumers and electric vehicle (EV) owners participating in the market, necessitating a robust optimization approach to formulate resilient offering and bidding strategies under uncertain market conditions. The proposed model is tailored for a distribution network equipped with a neighborhood battery, enabling prosumers to charge or discharge the battery profitably. Robust half-hourly-based offer and bid curves are submitted by prosumers and EV owners, reflecting their market price predictions and accommodating different levels of uncertainty, treated as scenarios. The social welfare maximization problem is formulated using a scenario-based approach, considering the received offer/bid curves to clear the market. Results show that considering the discharge capability of EVs leads to an increase in the overall social welfare of distribution networks. Additionally, when both the capabilities of discharge and export power are considered, it can be observed that the presence of a neighborhood battery can lead to an improvement of more than 35.83% in overall social welfare. • Employing robust optimization to drive prosumers and EV owners' strategies in markets • Ensuring robust solutions by incorporating offer/bid curves and AC OPF constraints • Assessing neighborhood battery impact on prosumers' bill and network constraints [ABSTRACT FROM AUTHOR]

A powerful thermal management scheme is the key to realizing the extremely fast charging of battery electric vehicles. In this scheme, a water-cooled plate is set at the bottom of the battery modules, which has a remarkable heat dissipation ability but increases the temperature difference between the top and bottom of the battery. This temperature difference problem is more obvious during the high-rate charge/discharge process; in particular, in the area of battery tabs, which are usually located far from the cooling plate, heat accumulates, risking thermal runaway. To address this problem, a thermal management method based on an ultrathin vapour chamber (UTVC) is proposed in this paper to improve temperature uniformity and reduce the highest temperature of batteries. According to the number of applied UTVCs, a unilateral UTVC (U-UTVC) and a bilateral UTVC (B-UTVC) cooling approach were presented, the effectiveness of which was verified by a set of thermal performance experiments. The test data show that the temperature differences in the batteries under the U-UTVC and B-UTVC schemes are 39.77% and 73.54% lower than those under the traditional method at a 25 °C ambient temperature and a 1.39C charge rate, respectively. The temperature difference on the battery surface with the B-UTVC scheme is approximately 3 °C at 25 °C and 40 °C ambient temperature. • An ultra-thin vapour chamber-based power battery thermal management is proposed to improve the temperature uniformity. • The methods have limited effect on battery volumetric specific energy, and the volumetric specific energy of battery is only reduced by 1.2% which is far less than reported investigations. • Temperature difference and maximum temperature of battery module of the presented scheme are reduced by 15.49% and 73.54%, respectively. [ABSTRACT FROM AUTHOR]