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Isolated bidirectional DC–DC converters are becoming increasingly important in various applications, particularly in the electric vehicle sector, due to their ability to achieve bidirectional power flow and their safety features. This paper aims to review the switch strategies and topologies of isolated bidirectional DC–DC converters, with a specific focus on their applications in the field of electric vehicles. From the perspective of topology, PWM-type isolated bidirectional DC–DC converters, dual active bridge converters, and resonant-type isolated bidirectional DC–DC converters constitute the three main categories of these converters. The paper further examines the traditional switch strategies of these converters and discusses how specific switch technologies, such as single-phase shift, expanding-phase shift, double-phase shift, and triple-phase shift, can enhance the overall performance of isolated bidirectional DC–DC converters. The paper meticulously examines the characteristics of each topology and control scheme, as well as their typical use cases in practical applications. Particularly, the paper delves into the applications of isolated bidirectional DC–DC converters in the electric vehicle sector and draws conclusions regarding their potential and trends in future electric vehicle technology. [ABSTRACT FROM AUTHOR]

This paper presents a charging architecture for the Reconfigurable Cascaded Multilevel converter, which was specifically designed for electric vehicle (EV) powertrain applications. The RCMC topology is capable of executing power conversion and actively managing battery systems concurrently. The active battery management is achieved using the Reconfigurable Battery Module, which regulates the serial connection of cells via a switch pattern. In this paper, the RCMC is directly interfaced with an AC three-phase power system, facilitating the dynamic control over battery cells charging. Its inherent design allows for the implementation of various charging algorithms, customizable to specific requirements, without necessitating additional intermediary power stages. Firstly, an overview of the RCMC topology is given, and an analysis to define the optimal filter inductance is carried out. Subsequently, after the AC system characteristics are explained, two charging algorithms are presented and described: one prioritizes State of Charge (SOC) balancing among battery cells, while the other focuses on minimizing power losses. Moreover, a time estimation computation for the RCMC is carried out considering a two-level AC charging station. The result is compared with the time required for a conventional battery pack. The results show a reduction of 10 s in charging time for a mere 20% increase in SOC. Finally, the experimental setup is presented and used to validate the efficacy of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The rising Greenhouse Gas (GHG) emissions stemming from the extensive use of automobiles across the globe represent a critical environmental challenge, contributing significantly to phenomena such as global warming and the deterioration of air quality. To address these challenges, there is a critical need for research and development in electric vehicles (EVs) and their associated charging infrastructure, including off-board and on-board chargers (OBCs). This paper aims to bridge the gaps in existing review literature by offering a comprehensive review of both integrated and non-integrated OBCs for EVs, based on the authors' knowledge at the time of writing. The paper begins by outlining trends in the EV market, including voltage levels, power ratings, and relevant standards. It then provides a detailed analysis of two-level and multi-level power converter topologies, covering AC-DC power factor correction (PFC) and isolated DC-DC topologies. Subsequently, it discusses single-stage and two-stage non-integrated OBC solutions. Additionally, various categories of integrated OBCs (iOBCs) are explored, accompanied by relevant examples. The paper also includes comparison tables containing technical specifications and key characteristics for reference and analysis. [ABSTRACT FROM AUTHOR]

This paper presents an overview of the status and future prospects of fuel-cell electric vehicles (FC-EVs). As global concerns about emissions escalate, FC-EVs have emerged as a promising substitute for traditional internal combustion engine vehicles. This paper discusses the fundamentals of fuel-cell technology considering the major types of fuel cells that have been researched and delves into the most suitable fuel cells for FC-EV applications, including comparisons with mainstream vehicle technologies. The present state of FC-EVs, ongoing research, and the challenges and opportunities that need to be accounted for are discussed. Furthermore, the comparison between promising proton-exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) technologies used in EVs provides valuable insights into their respective strengths and challenges. By synthesizing these aspects, the paper aims to provide a comprehensive understanding and facilitate decision-making for future advancements in sustainable FC-EV transportation, thereby contributing to the realization of a cleaner, greener, and more environmentally friendly future. [ABSTRACT FROM AUTHOR]

A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery's capacity was conserved at 25.5 °C, which is the highest operating temperature attained under the proposed strategy. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) energy trading has attracted a lot of attention and the number of electric vehicles (EVs) has increased in the past couple of years. Toward sustainable mobility, EVs meet the standard development goals (SDGs) for attaining a sustainable future in the transport sector. This development and increasing number of EVs creates an opportunity for prosumers to trade electricity. Considering this opportunity, this review article aims to provide an in-depth analysis of P2P energy trading of EVs using blockchain in centralized and decentralized networks, which enables prosumers to exchange energy directly with one another. The paper is aimed to provide the reader with a state-of-the-art review on the P2P energy trading for EVs, considering different blockchain algorithms that are practically implemented or still in the research phase. Moreover, the paper presents blockchain applications, current trends, and future challenges of EVs' energy trading. P2P energy trading for EVs using blockchain algorithms can be successfully implemented considering real-time scenarios and economically benefits smart sustainable societies. [ABSTRACT FROM AUTHOR]

The layout and configuration of urban infrastructure are essential for the orderly operation and healthy development of cities. With the promotion and popularization of new energy vehicles, the modeling and prediction of charging pile usage and allocation have garnered significant attention from governments and enterprises. Short-term demand forecasting for charging piles is crucial for their efficient operation. However, existing prediction models lack a discussion on the appropriate time window, resulting in limitations in station-level predictions. Recognizing the temporal nature of charging pile occupancy, this paper proposes a novel stacked-LSTM model called attention-SLSTM that integrates an attention mechanism to predict the charging demand of electric vehicles at the station level over the next few hours. To evaluate its performance, this paper compares it with several methods. The experimental results demonstrate that the attention-SLSTM model outperforms both LSTM and stacked-LSTM models. Deep learning methods generally outperform traditional time series forecasting methods. In the test set, MAE is 1.6860, RMSE is 2.5040, and MAPE is 9.7680%. Compared to the stacked-LSTM model, MAE and RMSE are reduced by 4.7%and 5%, respectively; while MAPE value decreases by 1.3%, making it superior to LSTM overall. Furthermore, subsequent experiments compare prediction performance among different charging stations, which confirms that the attention-SLSTM model exhibits excellent predictive capabilities within a six-step (2 h) window. [ABSTRACT FROM AUTHOR]

Global warming, pollution, and increasing energy demand have compelled electrification of the transport sector. Electric vehicles are not only an attractive and cleaner mode of transport, but they also possess the capacity to offer flexible storage alternative based on bidirectional vehicle-to-grid schemes. Vehicle-to-grid or V2G technology permits electric vehicles' batteries to store energy and discharge it back to the power grid during peak-load periods. However, the feasibility and economic viability of V2G is still a matter of concern and needs investigation. In this paper, the authors delved into the feasibility of V2G technology by analysing the real time-charging data of a V2G demonstration project named EV-elocity, located at the University of Nottingham campus in the UK. The authors analysed the charging data and trip-status data of two charging sites and put forward some insights regarding the feasibility of V2G and the behavioural traits of the vehicles. This paper will enlighten the research community regarding the feasibility and benefits of V2G in a real-world environment by analysing the charging/discharging and vehicle behaviour and reporting the opportunities and benefits of vehicle-to-grid technology. [ABSTRACT FROM AUTHOR]

Due to concerns with air pollution and climate change, governments and transport operators around the world have engaged in transforming their fossil-fueled vehicles into electric vehicles (EVs). It is essential to build a model for the electrifying process to minimize the operation costs. This paper presents a systematic analytical approach for the electrification of a fire ambulance service station. This approach begins with the selection of suitable EVs to replace the current service vehicles. Subsequently, an in-depth analysis is conducted to determine the practical utilization of EVs at the station. The model proposes two charging strategies: immediate charging upon an EVs' return and smart charging. Based on the chosen EVs and charging strategies, a comprehensive assessment of the load profiles for the planned EV charging station is performed. In accordance with the load profiles, a mathematical model to minimize the infrastructure and operation costs of the charging station is proposed. Various pricing schemes are compared to identify the most efficient pricing scheme for the charging station, and economic analyses of the EVs and traditional ambulance vehicles are proposed in this paper. The test results indicate that the progressive pricing scheme is well suited for immediate charging strategies, whereas smart charging should opt for the time-of-use pricing scheme. Selecting the appropriate pricing scheme has the potential to significantly reduce electric energy costs. [ABSTRACT FROM AUTHOR]

The battery pack is at the heart of electric vehicles, and lithium-ion cells are preferred because of their high power density, long life, high energy density, and viability for usage in relatively high and low temperatures. Lithium-ion batteries are negatively affected by overvoltage, undervoltage, thermal runaway, and cell voltage imbalance. The minimisation of cell imbalance is particularly important because it causes uneven power dissipation by each cell and, hence, temperature distribution that adversely impacts the battery lifetime. Several papers in the literature proposed advanced cell-balancing techniques to increase the effectiveness of basic cell-balancing approaches, reduce power losses, and reduce the number of components in balancing circuits. The new developments and optimisations over the last few years have been particularly intense due to the increased interest in battery technologies for several end-use applications. This paper reviews and discusses recent cell-balancing techniques or methods, covering their operating principles and the optimised utilisation of electrical components. [ABSTRACT FROM AUTHOR]

This paper presents the process of creating a model for electric vehicle (EV) energy consumption, enabling the rapid generation of results and the creation of energy maps. The most robust validation indicators were exhibited by an artificial intelligence method, specifically neural networks. Within this framework, two predictive models for EV energy consumption were developed for winter and summer conditions, based on actual driving cycles. These models hold particular significance for microscale road analyses. The resultant model, for test data in summer conditions, demonstrates validation indicators of an R2 of 86% and an MSE of 1.4, while, for winter conditions, its values are 89% and 2.8, respectively, confirming its high precision. The paper also presents exemplary applications of the developed models, utilizing both real and simulated microscale data. The results obtained and the presented methodology can be especially advantageous for decision makers in the management of city roads and infrastructure planners, aiding both cognitive understanding and the better planning of charging infrastructure networks. [ABSTRACT FROM AUTHOR]

The adoption of electric vehicles (EVs) has gained significant momentum in recent years as a sustainable alternative to traditional internal combustion engine vehicles. However, the efficient utilization of batteries in EVs, coupled with the growing demand for sustainable transportation, has posed complex challenges for battery management in the context of electric vehicle routing problems in a broad sense, which includes vehicle routing problems, team orienteering problems, and arc routing problems, all of them using EVs. This paper presents a comprehensive review of the state-of-the-art approaches, methodologies, and strategies for battery management in each of the aforementioned optimization problems. We explore the relevant factors influencing battery performance and the interplay between routing, charging, and energy management in the context of EVs. The paper also discusses the advances in optimization algorithms, vehicle-to-grid integration, and intelligent decision-making techniques aimed at enhancing the range, reliability, and sustainability of EV operations. Key findings indicate a paradigm shift towards addressing uncertainties, dynamic conditions, and synchronization challenges inherent in large-scale and dynamic routing problems within the context of EVs that require efficient battery management. [ABSTRACT FROM AUTHOR]

As penetration of EVs in the transportation sector is increasing, the demand for the mandatory installation of charging infrastructure also is increasing. In addition, renewable energy and energy storage systems (ESSs) are being reviewed for use in electric vehicle charging stations (EVCSs). In this paper, we present an optimal electricity trading volume and an optimal installation capacity of ESSs to maximize the daily profit of the EVCSs equipped with solar power generation when the EVCSs are licensed to sell energy to the power supplier during a specific time period. By formulating and solving the optimization problem of the EVCSs, this paper analyzes validation results for the different useful lives of ESSs, the peak power of a PV generator, and weather conditions at the Yangjae Solar Station and the Suseo Station public parking lot, Seoul, Republic of Korea. Furthermore, this paper validates that the daily expected profit of EVCSs with the proposed method outperforms the profit of conventional EVCSs which do not utilize ESSs. [ABSTRACT FROM AUTHOR]

The application of electrochemical cells as a source unit of electrical energy is rapidly growing—used in electric vehicles and other electric mobility devices, as well as in energy supply systems—as energy storage, often together with renewable energy sources. The key element of such systems is the power electronic converter used for DC energy storage and AC grid interfacing. It should be bidirectional to charge and discharge the battery when it is necessary. Two-stage battery interface converters are the most common; their DC-DC stage controls the battery current and adjusts voltage, but the DC-AC stage (inverter or frontend) controls the current in the grid. The use of unfolding inverters in two-stage battery interfaces can have some advantages. In this case, the DC-DC converter produces half-sinewave pulsating voltages and currents, but the unfolding circuit changes the polarity of the voltages and currents and produces no switching losses. Another trend of modern power electronics is the principle of partial power processing. In this case, power electronic converters deal only with a part of the total power; therefore, losses in such converters are reduced. This paper considers combining unfolding frontends with partial power DC-DC converters that enable the further reduction in losses. In this paper, it is shown that such implementation of the partial power conversion principle in semi-DC-AC systems is really possible based on the real-time matching of the voltage of the partial-power DC-DC converter, battery voltage (which depends on its state of charge) and the rectified instantaneous voltage of the AC grid. [ABSTRACT FROM AUTHOR]

This paper's research question is to evaluate the potential impact of large numbers of battery electric vehicles (BEVs) on the future electric grid, and whether the flexibility of BEV charging can induce enough system benefits to remunerate BEV users for the change in their recharging pattern. The considered scenario refers to the Italian situation and what might occur through the year 2040, where a share of BEV stock of 40% can be foreseen, as well as significant increases in PV and wind generation. Although this study is focused on Italy, its results are applicable, with minor differences, to several EU countries. This paper first shows that the future impact of increasing penetration of BEVs appears to be compatible with the expected growth of generation from renewable energy sources (RES) and the corresponding reduction in fossil fuel-based generation. It also gives an estimate of the CO2 emission reduction resulting from these changes, considering an unmanaged BEV charge profile and two different managed profiles that shift the car's charging period to hours of the day when they have no negative impact on the grid and maximize the utilization of RES. Finally, it shows an evaluation of the economic benefits of displacing private car charging ranging from 4 to 10 cEUR/kWh, which could be used as tariff incentives to stimulate this displacing in recharging time. [ABSTRACT FROM AUTHOR]

The transport sector is under scrutiny because of its significant greenhouse gas emissions. Essential strategies, particularly the adoption of zero- and low-emission vehicles powered by electricity, are crucial for mitigating emissions in road transport. Pickups, which are integral to Mexico's fleet, contribute to such emissions. Thus, implementing effective policies targeting pickups is vital for reducing air pollution and aligning with Mexico's decarbonization objectives. This paper presents a simulation model based on system dynamics to represent the adoption process of zero- and low-emission vehicles, with a focus on pickups and utilizing data from the Mexican case. Three policy evaluation scenarios are proposed based on the simulation model: business as usual; disincentives for zero- and low-emission vehicles; and incentives for unconventional vehicles. One of the most significant findings from this study is that even in a scenario with a greater number of vehicles in circulation, if the technology is fully electric, the environmental impact in terms of emissions is lower. Additionally, a comprehensive sensitivity analysis spanning a wide spectrum is undertaken through an extensive computational process, yielding multiple policy scenarios. The analysis indicates that to achieve a maximal reduction in the country's emissions, promoting solely hybrid electric vehicles and plug-in hybrid electric vehicles is advisable, whereas internal combustion engines, vehicular natural gas, and battery electric vehicles should be discouraged. [ABSTRACT FROM AUTHOR]

Many challenges have arisen as a result of the rapid growth of the electric vehicles (EVs) market, due to the lack of charging infrastructure capable of handling such a large number of EVs. To alleviate power grid system overloads and reduce the cost of corresponding infrastructure deployments, a direct vehicle-to-vehicle (V2V) energy exchange strategy has become an emerging research topic. In this paper, we formulate the problem of V2V energy charging on a time–space network and develop a dynamic-programming solution methodology for efficiently finding the solution. The algorithm can pair and route the energy supplier (ES) and the requester (ER) in such a way that maximizes the supplier's profit. Specifically, the ES is incentivized to rendezvous ERs at any encounter nodes in order to dispense the requested energy amount through platooning. Unlike existing V2V charging solutions, our approach involves charging while vehicles are in motion. We validate the effectiveness of our approach in maximizing the profit of the ES and reducing the incurred overhead on the ER in terms of increased trip time, distance, and energy consumption. [ABSTRACT FROM AUTHOR]

During the energy transition, new types of electrical equipment, especially power electronic devices, are proposed to increase the flexibility of electricity distribution grids. One type is the solid-state transformer (SST), which offers excellent possibilities to improve the voltage quality in electricity distribution grids and integrate hybrid AC/DC grids. This paper compares SST to conventional copper-based power transformers (CPT) with and without an on-load tap changer (OLTC) and with additional downstream converters. For this purpose, a corresponding electricity distribution grid is set up in the power system analysis tool DIgSILENT PowerFactory 2022. A DC generator like a photovoltaic system, a DC load like an electric vehicle fast charging station, and an AC load are connected. Based on load flow simulations, the four power transformers are compared concerning voltage stability during a generator-based and a load-based scenario. The results of load flow simulations show that SSTs are most valuable when additional generators and loads are to be connected to the infrastructure, which would overload the existing grid equipment. The efficiency of using SSTs also depends on the parameters of the electrical grid, especially the lengths of the low-voltage (LV) lines. In addition, a flowchart-based decision process is proposed to support the decision-making process for the appropriate power transformer from an electrical perspective. Beyond these electrical properties, an evaluation matrix lists other relevant criteria like characteristics of the installation site, noise level, expected lifetime, and economic criteria that must be considered. [ABSTRACT FROM AUTHOR]

Although technical improvements to engines and aftertreatment systems have the greatest impact on pollutant emissions, there is also potential for reducing emissions through driver behavior. This potential can be realized in the very short term, while better emission-control technologies only take effect once they have penetrated the market. In addition to a change in driving style, the vehicle owner's choice of vehicle technology and size class will also have an impact on the future emissions of the vehicle fleet. The effects of different driving styles, the tire choice, the vehicle size class, and propulsion technologies on energy consumption and tailpipe and non-exhaust emissions are analyzed in this paper for different traffic situations and start temperatures for cars with petrol and diesel combustion engines and for battery electric vehicles. The analysis is completed with the corresponding upstream emissions from fuel and electricity production. The analysis is based on a vehicle simulation using the Passenger car and Heavy-duty Emission Model (PHEM), which is based on a large database of vehicles created using measurements of real driving conditions. For the assessment of the driving style, a novel method was developed in an H2020 project, which reproduces a measured trip with a virtual eco-driver. Carbon dioxide equivalent emissions (CO2eq) increase with increasing vehicle size, but can be reduced by around 20% for conventional vehicles and 17% for battery electric vehicles (BEVs) through an environmentally conscious driving style. On average, BEVs have around 50% lower CO2eq emissions than conventional vehicles, if the emissions from vehicle production are also taken into account. On an average journey of 35 km, the cold start of modern diesel vehicles accounts for around half of the total NOx emissions, while the proportion of cold starts for petrol vehicles is around 25%. Tire and brake wear together generate a similar amount of PN23 emissions as the exhaust gases from new cars. [ABSTRACT FROM AUTHOR]

Advancements in electric vehicle technology have promoted the development trend of smart and low-carbon environmental protection. The design and optimization of electric vehicle braking systems faces multiple challenges, including the reasonable allocation and control of braking torque to improve energy economy and braking performance. In this paper, a multi-source braking force system and its control strategy are proposed with the aim of enhancing braking strength, safety, and energy economy during the braking process. Firstly, an ENMPC (explicit nonlinear model predictive control)-based braking force control strategy is proposed to replace the traditional ABS strategy in order to improve braking strength and safety while providing a foundation for the participation of the drive motor in ABS (anti-lock braking system) regulation. Secondly, a grey wolf algorithm is used to rationally allocate mechanical and electrical braking forces, with power consumption as the fitness function, to obtain the optimal allocation method and provide potential for EMB (electro–mechanical brake) optimization. Finally, simulation tests verify that the proposed method can improve braking strength, safety, and energy economy for different road conditions, and compared to other methods, it shows good performance. [ABSTRACT FROM AUTHOR]

As the global transition toward sustainable energy gains momentum, integrating electric vehicles (EVs), energy storage, and renewable energy sources has become a pivotal strategy. This paper analyses the interplay between EVs, energy storage, and renewable energy integration with Indonesia's grid as a test case. A comprehensive energy system modeling approach using PLEXOS is presented, using historical data on electricity generation, hourly demand, and renewable energy, and multiple scenarios of charging patterns and EV adoption. Through a series of scenarios, we evaluate the impact of different charging strategies and EV penetration levels on generation capacity, battery storage requirements, total system cost, renewable energy penetration, and emissions reduction. The findings reveal that optimized charging patterns and higher EV adoption rates, compared to no EVs adoption, led to substantial improvements in renewable energy utilization (+4%), emissions reduction (−12.8%), and overall system cost (−9%). While EVs contribute to reduced emissions compared to conventional vehicles, non-optimized charging behavior may lead to higher total emissions when compared to scenarios without EVs. The research also found the potential of vehicle to grid (V2G) to reduce the need for battery storage compared to zero EV (−84%), to reduce emissions significantly (−23.7%), and boost penetration of renewable energy (+10%). This research offers valuable insights for policymakers, energy planners, and stakeholders seeking to leverage the synergies between EVs and renewable energy integration to pursue a sustainable energy future for Indonesia. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are emerging as one of the pillars for achieving climate neutrality. They represent both a threat and an opportunity for the operation of the network. Used as flexible loads, they can favor the self-consumption of photovoltaic (PV) energy. This paper presents three EV charging tariff systems (TSs) based on the self-consumption of excess PV energy. The TS objectives are to increase the self-consumption rate (SCR) and thus indirectly decrease the charging cost of the EV users. Two of the proposed TSs correspond to an indirect control of EV charging. The third TS is a hybrid system where the charging power is controlled. The TS is designed using a series of rules that consider the momentary PV surplus and the charging power of each EV. The influence of the TS is simulated by considering real data from a PV collective self-consumption project in the Basque Country (Spain). The TS simulations performed with 6 months of data show a 13.1% increase in the SCR when applying the third TS, reaching an average of 93.09% for the SCR. In addition, the cost of EV charging is reduced by 25%. [ABSTRACT FROM AUTHOR]

The theoretical impact of the electric vehicle (EV) market share growth has been widely discussed with regards to technical and socioeconomic aspects in recent years. However, the prospection of EV scenarios is a challenge, and the difficulty increases with the granularity of the study and the set of variables affected by user behavior and regional aspects. Moreover, the lack of a robust database to estimate fast-charging stations' load curves, for example, affects the quality of planning, allocation, or grid impact studies. When this problem is evaluated on highways, the challenge increases due to the reduced number of trips related to the reduced number of charger units installed and the limited EVs range, which influence user anxiety. This paper presents a methodology to estimate the highway fast-charging station operation condition, considering regional and EV user aspects. The process is based in a block of traffic simulation, considering the traffic information and highway patterns composing the matrix solution model. Also, the output block estimates charging stations' operational conditions, considering infrastructure scenarios and simulated traffic. A Monte Carlo simulation is presented to model entrance rates and charging times, considering the PDF of stochastic inputs. The results are shown for the aspects of load curve and queue length for one case study, and a sensibility study was conducted to evaluate the impact of model inputs. [ABSTRACT FROM AUTHOR]

In recent years, with the growing number of EV charging stations integrated into the grid, optimizing the aggregated EV load based on individual EV flexibility has drawn aggregators' attention as a way to regulate the grid and provide grid services, such as day-ahead (DA) demand responses. Due to the forecast uncertainty of EV charging timings and charging energy demands, the actual delivered demand response is usually different from the DA bidding capacity, making it difficult for aggregators to profit from the energy market. This paper presents a two-layer online feedback control algorithm that exploits the EV flexibility with controlled EV charging timings and energy demands. Firstly, the offline model optimizes the EV dispatch considering demand charge management and energy market participation, and secondly, model predictive control is used in the online feedback model, which exploits the aggregated EV flexibility region by reducing the charging energy based on the pre-decided service level for demand response in real time (RT). The proposed algorithm is tested with one year of data for 51 EVs at a workplace charging site. The results show that with a 20% service level reduction in December 2022, the aggregated EV flexibility can be used to compensate for the cost of EV forecast errors and benefit from day-ahead energy market participation by USD 217. The proposed algorithm is proven to be economically practical and profitable. [ABSTRACT FROM AUTHOR]

Although the EVRP (Electric Vehicle routing problem) has promising results on the environmental scale, its implementation has proved challenging. The difficulty of the EVRP resides in the limited driving range of the electric vehicles, combined with the significant charging time. While the charging cost is less than the cost of fuel, this charge time adds to the overall travel time and may overlap with customers' time windows. All these factors increased the computational time exponentially and resulted in the need to overlook some constraints such as traffic congestion, road conditions, weather impact on energy consumption, and driving style, to name a few, in order to speed up execution time. While this method proved effective in accelerating the process of the EVRP, it did, however, render the approach unrealistic, as it steered far from real-life settings and made the approach unpredictable when facing dynamic and changing parameters. In this paper, we try to remedy this issue by proposing an approach in which we try to replicate real-life parameters such as heterogenous fleets, energy consumption, and infrastructure data. The objective of our approach was to minimize the total travel time, travel distance, energy consumed, and the number of vehicles deployed. To solve this problem, we propose a three-stages approach, in which the first stage consists of a newly developed dispatching approach where customers are assigned to vehicles. The second stage uses the genetic algorithm to find a set of optimal paths, and, finally, in the third stage, charging stations are inserted in the selected paths. Upon testing our approach on Solomon's instances, our approach proved effective in finding optimal solutions in a reasonable time for five- to fifteen-customer datasets. However, when trying to solve larger datasets, the approach was slowed down by the extreme number of constraints it had to satisfy. [ABSTRACT FROM AUTHOR]

This paper presents a case study of a lithium battery and fuel cell integrated powertrain system for a renewable energy vehicle. The performance analysis includes evaluating the energy consumption of the vehicle and the efficiency of the power generation components. When driven solely by the lithium battery at average speeds of 15 km/h and 20 km/h, it was observed that speed significantly influences the travel distance of the vehicle, with higher speeds resulting in lower mileage. The energy efficiency rates were found to be 89.3% and 85.7% at speeds of 15 km/h and 20 km/h, respectively, indicating an 18.1% decrease in efficiency from low to higher speeds. When the lithium battery is solely charged by the hydrogen fuel cell, the efficiency under test conditions reaches approximately 32.5%. In the "FC + B + SC" driving mode, which combines the use of the lithium battery, fuel cell, and solar panel to power the vehicle, the travel range can be extended to 50.62 km and 42.05 km, respectively, representing an increase of over 50%, with overall efficiencies of 63.8% and 60.7%, respectively. This hybrid powertrain system exhibits rapid dynamic response, high energy and power density, and enables longer travel distances for the renewable energy vehicle. [ABSTRACT FROM AUTHOR]

Considering the current world trends, the most challenging issue industry is facing revolves around how to reduce the power consumption of electronic systems. Since the invention of computers, electrical energy consumption has increased dramatically; this is due to the emergence of new systems in industry. Systems like industrial robots and autonomous vehicles—including electric vehicles (EVs) and unmanned aerial vehicles (UAVs)—have had a great impact in making human life easier but have also led to higher energy consumption. At present, researchers and developers are actively seeking solutions and patents to optimize the energy consumption of the mentioned systems and generate savings, with the goal of reducing their environmental impact and improving their efficiency and effectiveness. From the literature review, papers related to energy optimization and energy consumption are considered vital, and a huge number of research publications and survey papers discuss it. This paper presents a systematic review of the classification and analysis of various methodologies and solutions that have been developed to enhance the energy performance of robotic systems, focusing on industrial robots, autonomous vehicles, and embedded systems. The aim of this research is to provide a reference point for the existing methods, techniques, and technologies that are available. It compares and evaluates different hardware and software methods related to industrial robots, autonomous vehicles, and embedded systems, highlighting the possible future perspectives in the field. [ABSTRACT FROM AUTHOR]

This paper presents an experimental measurement of the heat transfer coefficient (HTC) in a direct, oil-cooled lithium-ion battery at low Reynolds numbers. As demands on the electric vehicle battery pack increase, the role of thermal management to safeguard the pack becomes more important. Therefore, it is expected that various means for enhancing the HTC are sought. One way to increase the HTC is by shifting from air cooling to liquid cooling. The application of direct oil cooling in batteries has not yet been implemented. This paper explores this by developing the concept and an experimental steady-state technique to measure the HTC for direct oil cooling on a cylindrical 18650-cell battery at low Reynolds numbers. The experimental measurements are validated against known empirical correlations in the literature, showing that, despite the complex arrangement of cylindrical battery cells in packs, the classical correlations can be a useful tool to develop an oil-cooled battery thermal management system. A simplified correlation was also developed. [ABSTRACT FROM AUTHOR]

As urbanization increases, streetlights have become significant consumers of electrical power, making it imperative to develop effective control methods for sustainability. This paper offers a comprehensive review on control methods of smart streetlight systems, setting itself apart by introducing a novel light scheme framework that provides a structured classification of various light control patterns, thus filling an existing gap in the literature. Unlike previous studies, this work dives into the technical specifics of individual research papers and methodologies, ranging from basic to advanced control methods like computer vision and deep learning, while also assessing the energy consumption associated with each approach. Additionally, the paper expands the discussion to explore alternative functionalities for streetlights, such as serving as communication networks, environmental monitors, and electric vehicle charging stations. This multidisciplinary research aims to be a pivotal resource for both academics and industry professionals, laying the groundwork for future innovation and sustainable solutions in urban lighting. [ABSTRACT FROM AUTHOR]

The integration of renewable energy systems in buildings leads to a reduction in energy bills for end users and a reduction in the carbon footprint of such buildings, usually referred to as prosumers. In addition, the installation of charging points for the electric vehicles of people working or living in these buildings can further improve the energy efficiency of the whole system if innovative technologies, such as vehicle-to-building (V2B) technologies, are implemented. The aim of this paper is to present an Energy Management System (EMS) based on mathematical programming that has been developed to optimally manage a prosumer building equipped with photovoltaics, a micro wind turbine and several charging points for electric vehicles. Capabilities curves of renewable power plant inverters are modelled within the EMS, as well as the possibility to apply power curtailment and V2B. The use of V2B technology reduces the amount of electricity purchased from the public grid, while the use of smart inverters for the power plants allows zero reactive power to be drawn from the grid. Levelized cost of electricity (LCOE) is used to quantify curtailment costs, while penalties on reactive power absorption from the distribution network are evaluated in accordance with the current regulatory framework. Specifically, the model is applied to a prosumer building owned by the postal service in a large city in Italy. The paper reports the main results of the study and proposes a sensitivity analysis on the number of charging stations and vehicles, as well as on the consideration of different typical days characterized by different load and generation profiles. This paper also investigates how errors in forecasting energy production from renewable sources impact the optimal operation of the whole system. [ABSTRACT FROM AUTHOR]

Transportation electrification is pivotal for achieving energy security and emission reduction goals. Electric vehicles (EVs) are at the forefront of this transition, driving the development of new EV technologies and infrastructure. As this trend gains momentum, it becomes essential to enhance the quality of service (QoS) of EVs to encourage their widespread adoption. This paper has been structured with two primary aims to effectively address the above timely technological needs. Firstly, it comprehensively reviews the various QoS factors that influence EVs' performance and the user experience. Delving into these factors provides valuable insights into how the QoS can be improved, thereby fostering the increased use of EVs on our roads. In addition to the QoS, this paper also explores recent advancements in communication technologies vital for facilitating in-formation exchanges between EVs and charging stations. Efficient communication systems are crucial for optimizing EV operations and enhancing user experiences. This paper presents expert-level technical details in an easily understandable manner, making it a valuable resource for researchers dedicated to improving the QoS of EV communication systems, who are tirelessly working towards a cleaner, more efficient future in transportation. It consolidates the current knowledge in the field and presents the latest discoveries and developments, offering practical insights for enhancing the QoS in electric transportation. A QoS parameter reference map, a detailed classification of QoS parameters, and a classification of EV communication technology references are some of the key contributions of this review paper. In doing so, this paper contributes to the broader objectives of promoting transportation electrification, enhancing energy security, and reducing emissions. [ABSTRACT FROM AUTHOR]

This research paper presents an enhanced economic load dispatch (ELD) approach using the Teaching–Learning-Based Optimization (TLBO) algorithm for 10 thermal units, examining the impact of Plug-in Electric Vehicles (PEVs) in different charging scenarios. The TLBO algorithm was utilized to optimize the ELD problem, considering the complexities associated with thermal units. The integration of PEVs in the load dispatch optimization was investigated, and different charging profiles and probability distributions were defined for PEVs in various scenarios, including overall charging profile, off-peak charging, peak charging, and stochastic charging. These tables allow for the modeling and analysis of PEV charging behavior and power requirements within the power system. By incorporating PEVs, additional controllable resources were introduced, enabling more effective load management and grid stability. The comparative analysis showcases the advantages of the TLBO-based ELD model with PEVs, demonstrating the potential of coordinated dispatch strategies leveraging PEV storage and controllability. This paper emphasizes the importance of integrating PEVs into the load dispatch optimization process, utilizing the TLBO algorithm, to achieve economic and reliable power system operation while considering different PEV charging scenarios. [ABSTRACT FROM AUTHOR]

With the update of the power transaction mode and the access of increasing electric vehicles with high randomness to the power grid, the existing carbon emission flow calculation method cannot consider the influence of carbon emission due to the fluctuation in electric vehicle charging and various transaction modes. Given the above shortcomings, this paper proposes an improved carbon emission flow model considering electric vehicle charging fluctuations and hybrid power transactions. The model first considers different transaction modes, allocates the network loss to both sides of power generation, and forms a lossless network, realizing the decoupling of the bilateral transaction mode, pool transaction mode, and network loss and then calculating the day-ahead network's carbon emission. Then, the changes in different transaction modes caused by the fluctuation in electric vehicle charging under different transaction modes are analyzed and the nodes of 'spontaneous change' are found to form a day-ahead intra-day deviation network and calculate its carbon emission flow. Finally, the calculation results are combined to obtain an improved power system carbon emission flow considering electric vehicle charging fluctuations and transaction modes. In this paper, the 33-node system is used for verification. [ABSTRACT FROM AUTHOR]

The establishment of near-autonomous micro-grids in commercial or public building complexes is gaining increasing popularity. Short-term storage capacity is provided by means of large battery installations, or, more often, by the employees' increasing use of electric vehicle batteries, which are allowed to operate in bi-directional charging mode. In addition to the above short-term storage means, a long-term storage medium is considered essential to the optimal operation of the building's micro-grid. The most promising long-term energy storage carrier is hydrogen, which is produced by standard electrolyzer units by exploiting the surplus electricity produced by photovoltaic installation, due to the seasonal or weekly variation in a building's electricity consumption. To this end, a novel concept is studied in this paper. The details of the proposed concept are described in the context of a nearly Zero Energy Building (nZEB) and the associated micro-grid. The hydrogen produced is stored in a high-pressure tank to be used occasionally as fuel in an advanced technology hydrogen spark ignition engine, which moves a synchronous generator. A size optimization study is carried out to determine the genset's rating, the electrolyzer units' capacity and the tilt angle of the rooftop's photovoltaic panels, which minimize the building's interaction with the external grid. The hydrogen-fueled genset engine is optimally sized to 40 kW (0.18 kW/kWp PV). The optimal tilt angle of the rooftop PV panels is 39°. The maximum capacity of the electrolyzer units is optimized to 72 kW (0.33 kWmax/kWp PV). The resulting system is tacitly assumed to integrate to an external hydrogen network to make up for the expected mismatches between hydrogen production and consumption. The significance of technology in addressing the current challenges in the field of energy storage and micro-grid optimization is discussed, with an emphasis on its potential benefits. Moreover, areas for further research are highlighted, aiming to further advance sustainable energy solutions. [ABSTRACT FROM AUTHOR]

This paper presents a two-level hierarchical control method for the power distribution between the hybrid energy storage system (HESS) and the main dc bus of a microgrid for ultrafast charging of electric vehicles (EVs). The HESS is composed of a supercapacitor and a battery and is an essential part to fulfill the charging demand of EVs in a microgrid made up of a 220 V RMS ac bus, two dc buses of 600 V and 1500 V, respectively, and four charging points. A state machine defines the four operating modes of the HESS and establishes the conditions for the corresponding transitions among them, namely, charging the battery and the supercapacitor from the bus, injecting the current from the HESS into the 1500 V dc bus to ensure the power balance in the microgrid, regulating the bus voltage, and establishing the disconnection mode. The primary level of the control system regulates the current and voltage of the battery, supercapacitor, and dc bus, while the secondary level establishes the operating mode of the HESS and provides the appropriate references to the primary level. In the primary level, sliding mode control (SMC) is used in both the battery and supercapacitor in the inner loop of a cascade control that implements the standard constant current–constant voltage (CC-CV) charging protocol. In the same level, linear control is applied in the CV phase of the protocol and for bus voltage regulation or the current injection into the bus. PSIM simulations of the operating modes and their corresponding transitions verify the theoretical predictions. [ABSTRACT FROM AUTHOR]

Micro-mobility plays an increasingly important role in the current energy transition thanks to its low energy consumption and reduced contribution to urban congestion. In this scenario, fuel cell hybrid electric vehicles have several advantages over state-of-the-art battery electric vehicles, such as increased driving ranges and reduced recharge times. In this paper, we study the conversion of a commercial electric moped (Askoll eS3®) into a fuel cell hybrid electric vehicle by finding the optimal design of the components through an optimization methodology based on backward dynamic programming. This optimal design and operation strategy can also be implemented with a rules-based approach. The results show that a system composed of a 1 kW proton exchange membrane fuel cell, a 2000 Sl metal hydride hydrogen tank, and a 240 Wh buffer battery can cover the same driving range as the batteries in an electric moped (119 km). Such a hybrid system occupies considerably less volume (almost 40 L) and has a negligibly higher mass. The free volume can be used to extend the driving range up to almost three times the nominal value. Moreover, by using a high-pressure composite tank, it is possible to increase the mass energy density of the onboard energy storage (although compression can require up to 10% of the hydrogen's chemical energy). The fuel cell hybrid electric vehicle can be recharged with green hydrogen that is locally produced. In detail, we analyze a residential scenario and a shared mobility scenario in the small Italian city of Viterbo. [ABSTRACT FROM AUTHOR]

Electric vehicles are increasingly appearing on Polish roads due to a number of technical, legal and marketing conditions. However, electromobility is developing primarily in urban areas, mainly due to the unevenly developed infrastructure for charging vehicle batteries and the power grid. Therefore, solutions should be created that use the existing power infrastructure, including the use of railway power infrastructure (RPI). The railway power network covers a significant part of the country, including forest areas, and, above all, it very often intersects with road infrastructure or runs along roads. This paper raises issues related to the possibility of using RPI to charge the batteries of electric vehicles. After characterizing the technical, operational and legal requirements related to these technical systems, a concept of an electric vehicle charging system using RPI was developed, along with a demonstration of the possibility of its implementation, which was simulated using mathematical models developed by the authors. [ABSTRACT FROM AUTHOR]

The silicon carbide (SiC) inverter brings great advantages to the motor drive systems of new energy vehicles; however, severe challenges to the bearings also happen. The high dc bus voltage and switching frequency of SiC inverter can increase the discharge frequency and energy when the bearing grease film collapses. As a result, the bearing suffers severe electric corrosion, and the service life of the motor drive system can be shortened. In this paper, the characteristics of common-mode voltage and bearing voltage are analyzed, firstly under space vector pulse width modulation (SVPWM). After that, the common-mode equivalent circuit model of the motor drive system is established. The frequency characteristics of bearing voltage are revealed, and the safe working area is determined. Then, the frequency characteristics of bearing voltage and current are verified based on IGBT and SiC inverters in experiments. After that, by designing a common-mode filter, the bearing voltage and current are significantly attenuated. Furthermore, the active zero state PWM (AZSPWM) is adopted to reduce the common-mode voltage from the inverter. At the same time, combined with the common-mode filter, the bearing voltage and current are further reduced. The experimental results show that the switching frequency has a decisive effect on the amplitude of bearing voltage and current. The bearing voltage can be attenuated to around half of the reference bearing voltage by using the common-mode filter and AZSPWM strategy, respectively. The combination of the common-mode filter and AZSPWM strategy can reduce the bearing voltage to around one-fourth of the reference bearing voltage, which can effectively reduce the breakdown time and discharge energy of the grease oil film. [ABSTRACT FROM AUTHOR]

As the automotive industry transitions from internal combustion engine vehicles to the era of electric cars, extensive research is being conducted in the field of electric vehicles. While a significant portion of this research focuses on the electrification of passenger cars, commercial vehicles have experienced relatively modest changes towards electric propulsion. Particularly, challenges related to power and efficiency have prompted a concentrated effort in addressing these issues. However, improvements in the efficiency of motors and inverters are reaching their limits, necessitating the development of multi-speed transmissions for electric commercial vehicles to enhance overall system efficiency. In this paper, the development of a 4-speed transmission with a synchronizer designed for electric commercial vehicles is presented as part of a project. A transmission shift map was developed, and verification of increased power and efficiency was conducted through a comparison with the existing product (a pneumatic 4-speed internal combustion engine transmission) installed in the target commercial vehicle. The study utilized vehicle dynamics, component modeling, and simulation environments to assess the improvements in performance. [ABSTRACT FROM AUTHOR]

This paper proposes a methodological way to compensate for the imbalance between energy generation and consumption using a battery block from electric vehicles as an energy reservoir through the well-known vehicle-to-grid system (V2G). This method is based on a simulation process developed by the authors that takes into consideration the daily fluctuations in energy consumption as well as the power level generated by an energy source, either conventional, renewable, or hybrid. This study shows that for very large electric vehicle fleets, the system is rendered non-viable, since the remaining energy in the battery block that allows the electric vehicle to be usable during the daytime avoids having to compensate for the energy grid imbalance, only allowing it to cover a percentage of the energy imbalance, which the proposed methodology may optimize. The analysis of the proposed methodology also shows the viability of the system when being applied to a small fleet of electric vehicles, not only compensating for the energy imbalance but also preserving the required energy in the battery of the electric vehicle to make it run. This method allows for predicting the optimum size of an electric vehicle battery, which depends on the energy generation level, coverage factor of the energy imbalance, and size of the electric vehicle fleet. [ABSTRACT FROM AUTHOR]

The electrification of transport is expected to progressively replace significant shares of light duty mobility, especially in large cities. The European Alternative Fuel Infrastructure Regulation (AFIR) aims to drive the adoption of electric mobility by establishing specific targets for charging point deployment. Innovative charging concepts may complement and accelerate the uptake of this fundamental part of the urban mobility transition. In this paper, one such innovative concept is described and its potential impact is assessed. The core idea involves integrating charging points into existing city tramway infrastructures. Turin's tramway network is taken as a representative case study. The proposed technical solution encompasses a charging hub powered by four isolated DC/DC converters of 50 kW, directly connected to the DC tramway distribution line. Three of these constitute the heart of a 150 kW charger, while the fourth acts as voltage regulator. This native DC installation greatly simplifies the architecture of the DC chargers. Using a conservative approach, it was estimated that a single recharging station could charge more than 60 vehicles daily. This highly scalable and replicable solution, with the potential for over 100 conversion substations across Italy, would enable the installation of numerous high-power chargers in urban settings. Furthermore, additional benefits could be realized through enhanced recovery of kinetic energy from trams, which is currently dissipated on-board. [ABSTRACT FROM AUTHOR]

Battery electric trucks (BETs) represent a well-suited option for decarbonizing road freight transport to achieve climate targets in the European Union. However, lower ranges than the daily distance of up to 700 km make charging stops mandatory. This paper presents an online algorithm for optimal dynamic charging strategies for long-haul BET based on a dynamic programming approach. In several case studies, we investigate the advantages optimal strategies can bring compared to driver decisions. We further show which charging infrastructure characteristics in terms of charging power, density, and charging station availability should be achieved for BETs in long-haul applications to keep the additional time required for charging stops low. In doing so, we consider the dynamic handling of occupied charging stations for the first time in the context of BET. Our findings show that, compared to driver decisions, optimal charging strategies can reduce the time loss by half compared to diesel trucks. To keep the time loss compared to a diesel truck below 30 min a day, a BET with a 500 kWh battery would need a charging point every 50 km on average, a distributed charging power between 700 and 1500 kW, and an average charger availability above 75%. The presented method and the case studies' results' plausibility are interpreted within a comprehensive sensitivity analysis and subsequently discussed in detail. Finally, we transformed our findings into concrete recommendations for action for the efficient rollout of BETs in long-haul applications. [ABSTRACT FROM AUTHOR]

Battery thermal management systems (BTMSs) ensure that lithium-ion batteries (LIBs) in electric vehicles (EVs) are operated in an optimal temperature range to achieve high performance and reduce risks. A conventional BTMS operates either as an active system that uses forced air, water or immersion cooling, or as a complete passive system without any temperature control. Passive systems function without any active energy supply and are therefore economically and environmentally advantageous. However, today's passive BTMSs have limited cooling performance, which additionally cannot be controlled. To overcome this issue, an innovative BTMS approach based on heat pipes with an integrated thermal switch, developed by the Fraunhofer Cluster of Excellence Programmable Materials (CPM), is presented in this paper. The suggested BTMS consists of switchable heat pipes which couple a passive fin-based cold plate with the battery cells. In cold state, the battery is insulated. If the switching temperature is reached, the heat pipes start working and conduct the battery heat to the cold plate where it is dissipated. The environmental benefits of this novel BTMS approach were then analysed with a Life Cycle Assessment (LCA). Here, a comparison is made between the suggested passive and an active BTMS. For the passive system, significantly lower environmental impacts were observed in nearly all impact categories assessed. It was identified as a technically promising and environmentally friendly approach for battery cooling in EVs of the compact class. Furthermore, the results show that passive BTMS in general are superior from an environmental point of view, due their energy self-sufficient nature. [ABSTRACT FROM AUTHOR]

With the continual expansion of urban road networks and global commitments to net zero, electric vehicles (EVs) have been considered to be the most viable solution to decarbonize the transportation sector. In recent years, the electric road system (ERS) has been introduced and piloted in a few countries and regions to decarbonize heavy-duty vehicles. However, little research has been carried out on its reliability. This paper fills the gap and investigates the reliability of electric truck power supply systems for electric road (ETPSS–ER), which considers both the power system and truck traffic networks. First, a brief introduction of electric roads illustrates the working principle of EV charging on roads. Then, an optimized electric truck (ET) travel pattern model is built, based on which the corresponding ET charging load demand, including both static charging and dynamic charging, is conducted. Then, based on the new ET travel pattern model, a daily travel-pattern-driven Monte Carlo simulation-based reliability assessment method for ETPSS–ER system is presented. Case studies based on the IEEE RBTS system shows that ETs driving on ERS systems can meet the daily travel demands. The case studies also examine the impacts of increasing number of ETs, extra wind power, and battery energy storage systems (BESS) on the reliability of ERS power systems. [ABSTRACT FROM AUTHOR]

In the era of burgeoning electric vehicle (EV) popularity, understanding the patterns of EV users' behavior is imperative. This paper examines the trends in household charging sessions' timing, duration, and energy consumption by analyzing real-world residential charging data. By leveraging the information collected from each session, a novel framework is introduced for the efficient, real-time prediction of important charging characteristics. Utilizing historical data and user-specific features, machine learning models are trained to predict the connection duration, charging duration, charging demand, and time until the next session. These models enhance the understanding of EV users' behavior and provide practical tools for optimizing the EV charging infrastructure and effectively managing the charging demand. As the transportation sector becomes increasingly electrified, this work aims to empower stakeholders with insights and reliable models, enabling them to anticipate the localized demand and contribute to the sustainable integration of electric vehicles into the grid. [ABSTRACT FROM AUTHOR]

The paper presents a novel approach for predicting battery energy consumption in electric city buses (e-buses) by means of a trip-based data-driven regression model. The model was parameterized based on the data collected by running a physical experimentally validated e-bus simulation model, and it consists of powertrain and heating, ventilation, and air conditioning (HVAC) system submodels. The main advantage of the proposed approach is its reliance on readily available trip-related data, such as travel distance, mean velocity, average passenger count, mean and standard deviation of road slope, and mean ambient temperature and solar irradiance, as opposed to the physical model, which requires high-sampling-rate driving cycle data. Additionally, the data-driven model is executed significantly faster than the physical model, thus making it suitable for large-scale city bus electrification planning or online energy consumption prediction applications. The data-driven model development began with applying feature selection techniques to identify the most relevant set of model inputs. Machine learning methods were then employed to achieve a model that effectively balances accuracy, simplicity, and interpretability. The validation results of the final eight-input quadratic-form e-bus model demonstrated its high precision and generalization, which was reflected in the R2 value of 0.981 when tested on unseen data. Owing to the trip-based, mean-value formulation, the model executed six orders of magnitude faster than the physical model. [ABSTRACT FROM AUTHOR]

The uncontrolled charging of electric vehicles may cause damage to the electrical system as the number of electric vehicles continues to rise. This paper aims to construct a new model of the power system and investigates the rational regulation and efficient control of electric vehicle battery charging at electric vehicle exchange battery stations in response to the real-time grid-side supply situation. Firstly, a multi-objective optimization strategy is established to meet the day-ahead forecasted swap demand and grid-side supply with the maximization of day-ahead electric vehicle battery swapping station (BSS) revenue in the core. Secondly, considering the variable tariff strategy, a two-layer Model Predictive Control (MPC) coordinated control system under real-time conditions is constructed with the objective function of maximizing the revenue of BSS and smoothing the load fluctuation of the power system. Then, the day-ahead optimization results are adopted as the reference value for in-day rolling optimization, and the reference value for in-day optimization is dynamically adjusted according to the real-time number of electric car changes and power system demand. Finally, verified by experimental simulation, the results show that the day-ahead-intraday optimization model can increase the economic benefits of BSS and reduce the pressure on the grid to a certain extent, and it can ensure the fast, accurate, and reasonable allocation of batteries in BSS, and realize the flexible, efficient, and reasonable distribution of batteries in BSS. [ABSTRACT FROM AUTHOR]

As the prevalence of electric vehicles (EVs) continues to grow, their charging and discharging behaviors pose a challenge to the stable operation of power systems. Therefore, this paper analyzes the charging demand of EV users through GPS trajectory data and proposes an EV-discharging-optimization model based on vehicle-to-grid interaction (V2G). Firstly, the spatial–temporal distribution of EV-charging demand is obtained by cleaning and mining the big data of traveling vehicles, considering dynamic energy consumption theory and users' willingness; secondly, a probabilistic model of EV users' participation in V2G-demand response is constructed based on expected utility theory, which both considers the heterogeneity of users and reflects the interactive influence of users' decisions; finally, a scheduling model of EV discharging in the regional grid is established. The results show that the proposed model can explore the potential of user participation in V2G in the study area, and the V2G response resources can reduce the grid fluctuation and enable users to obtain certain benefits, which achieves a win–win situation between the grid side and the user side. [ABSTRACT FROM AUTHOR]

This paper presents both the techno-economic planning and a comprehensive sensitivity analysis of an off-grid fully renewable energy-based microgrid (MG) intended to be used as an electric vehicle (EV) charging station. Different possible plans are compared using technical, economic, and techno-economic characteristics for different numbers of wind turbines and solar panels, and both single and hybrid energy storage systems (ESSs) composed of new Li-ion, second-life Li-ion, and new lead–acid batteries. A modified cost of energy (MCOE) index including EVs' unmet energy penalties and present values of ESSs is proposed, which can combine both important technical and economic criteria together to enable a techno-economic decision to be made. Bi-objective and multi-objective decision-making are provided using the MCOE, total met load, and total costs in which different plans are introduced as the best plans from different aspects. The number of wind turbines and solar panels required for the case study is obtained with respect to the ESS capacity using weather data and assuming EV demand according to the EV population data, which can be generalized to other case studies according to the presented modelling. Through studies on hybrid-ESS-supported MGs, the impact of two different global energy management systems (EMSs) on techno-economic characteristics is investigated, including a power-sharing-based and a priority-based EMS. Single Li-ion battery ESSs in both forms, new and second-life, show the best plans according to the MCOE and total met load; however, the second-life Li-ion shows lower total costs. The hybrid ESSs of both the new and second-life Li-ion battery ESSs show the advantages of both the new and second-life types, i.e., deeper depths of discharge and cheaper plans. [ABSTRACT FROM AUTHOR]

The extreme environmental issues and the resulting need to save energy have turned attention to the electrification of energy applications. One of the key components involved in energy efficiency improvements is the appropriate conception and manufacturing of electric machines. This paper overviews the electromagnetic analysis governing the behavior of permanent magnets that enable substantial efficiency gains in recent electric machine developments. Particular emphasis is given to modeling the properties and losses developed in permanent magnets in emerging high speed applications. In addition, the investigation of properties and harmonic losses related to ferromagnetic materials constituting the machine magnetic circuits are equally analyzed and discussed. The experimental validation of the implemented methodologies and developed models with respect to the obtained precision is reported. The introduction of mixed numerical techniques based on the finite element method intended to appropriately represent the different physical phenomena encountered is outlined and discussed. Finally, fast and accurate simulation techniques including aggregated lumped parameter models considering harmonic losses associated with inverter supplies are discussed. [ABSTRACT FROM AUTHOR]