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The governmental subsidy is an important instrument for catalyzing the early adoption of Electric Vehicles (EVs)}. A sustainable subsidy policy must balance between promoting EVs and expanding the charging infrastructure. This paper proposes a dynamic game approach to accelerate the adoption of EV technology. The government observes two correlated processes - the penetration of the EV market and the expansion of the charging infrastructure. It finds an optimal strategy that maximizes the cumulative social benefit in the face of uncertainty. A case study of China's EV industry finds that \blue{the established EV subsidy policies are inadequate to support EVs' growth over the next decades}. In contrast, the optimal subsidies will achieve \blue{the target EV market share and ensure the accessibility to charging infrastructure by 2030}. This non-myopic strategy can prioritize the infrastructure expansion, eliminate policy enforcement's monitoring costs, prevent compensation evasion, and help achieve long-term targets by making proactive regulatory policies.

The conventional hard-switching converters suffer from the limitations like the upper limit on switching frequency, high electromagnetic interference (EMI), more switching losses, large size, increased weight and low efficiency. To overcome these limitations, resonant converters are popularly used in chargers of electric vehicles (EVs). However, the detailed classification of resonant converters used in EVs is not sufficiently discussed in the literature. The guideline to select a resonant converter based topology required to charge an EV on the basis of its rating is not mentioned. To fill this gap, this paper presents a state-of-art literature survey of various resonant converter based topologies used in chargers of EVs. This paper focuses on a detailed classification of resonant converters used in the second stage of EV chargers. Further, it provides a guideline to designers to choose a converter topology used in the first stage and the second stage of EV charger required based on wattage, unidirectional and bidirectional power flow. Depending on the number of reactive elements present in a given resonant converter topology, these are classified as two-element, three-element, and multi-element resonant converters. Depending upon the connection of inductive (L) and capacitive (C) elements with respect to transformer winding, these converter topologies are further categorized as series, parallel (two-elements), inductor–inductor–capacitor (LLC) (three-element) and capacitor–inductor–inductor–capacitor (CLLC) (Multi-elements). However, the LLC type resonant converters offer high efficiency, zero-voltage switching (ZVS turn-on, turn-off) and low voltage stress on switches and high power density. Therefore, this paper mainly focuses on LLC type resonant converter topology. In addition, various modulation schemes and control schemes for LLC, CLLC resonant converter along with control of active power and reactive power are discussed for vehicle-2-grid (V2G) mode of operation.

Data availability: Data will be made available on request. Copyright © 2022 The Author(s). Many countries are observing significant growth rates in electric vehicle (EV) uptake, often backed by financial incentives or regulation and legislation. The availability of large multi-charger sites for rapid EV charging with an experience similar to conventional refueling refuelling stations lowers the barrier to acceptance for drivers considering the switch to using an EV. The question arises about how to size such a facility at the design and planning stage, as well as accommodating growth in the number of EVs in daily use. One of the important factors is the vehicle arrival rate and the corresponding power and energy demand. EV charging is a function of several parameters, all of which are stochastic in nature, such as the vehicle daily travelled distance, charging start time and the required energy. To account for uncertainty in the parameters, a stochastic model has been designed to simulate realistic vehicle arrival rates. The model accounts for EVs coming from the site catchment area and opportunistic charging from passing traffic traveling travelling on the major roads adjacent to the site, the seasonality of parameters, and charging at places other than the site (competitive charging). The model produced plausible EV arrival patterns for both local and passing traffic, and reproduced the characteristic power demand at the case study site. All estimates incorporate uncertainty, reflecting realistic variability of the important parameters. The model in independent of location, uses open-source data, and is structured flexibly, making it adaptable to new sites as part of the technical and business planning process. UK Research Council (UKRI) through Innovate UK under grant No. TS/T006471/1.

The transportation sector is one of the most prevalent fossil fuel users worldwide. Therefore, to mitigate the impacts of carbon-dioxide emissions and reduce the use of non-environmentally friendly traditional energy resources, the electrification of the transportation system, such as the development of electric vehicles (EV), has become crucial. For impeccable EVs deployment, a well-developed charging infrastructure is required. However, the optimal placement of fast charging stations (FCSs) is a critical concern. Therefore, this article provides a functional approach for identifying the optimal location of FCSs using the east delta network (EDN). In addition, the electrical distribution network's infrastructure is susceptible to changes in electrifying the transportation sector. Therefore, actual power loss, reactive power loss, and investment cost are three areas of consideration in deploying FCSs. Furthermore, including FCSs in the electricity distribution network increases the energy demand from the electrical grid. Therefore, this research paper recommends integrating solar-based distributed generations (SDGs) at selected locations in the distribution network, to mitigate the burden of FCSs on the system. Hence, making the system self-sustaining and reliable. In addition, the reliability of the distribution system is also analyzed after deploying the FCSs and SDGs. Furthermore, six case studies (CS) have been proposed to deploy FCSs with or without DG integration. Consequently, the active power loss went from 1014.48 kW to 829.68 kW for the CS-6. 2022 The Author(s) This publication was made possible by NPRP grant # [ NPRP-13S-0108-20008 ] from the Qatar National Research Fund (a member of Qatar Foundation) . The statements made herein are solely the responsibility of the authors. The APC is funded by Qatar National Library, Doha, Qatar . Scopus

In this paper, we aim to examine people's EV purchase intention in Hong Kong, an Asian compact city, and how the influential factors are different from the Western context, for which a low-density Nordic context, Denmark, was chosen. To achieve this, we conducted a survey in Hong Kong to understand the effects of several key factors, including subjective norms, personal norms, and perceptions and knowledge about EVs. Structural equation modelling was employed and multi-group analysis was conducted. Our findings show that EV purchase intentions in Hong Kong are highly value-driven with the assimilation to social expectations directly and strongly encouraging individuals' EV acceptance, which is different from the European case where the adoption desire is built upon the assessment of usage difficulty. In addition, range anxiety poses a direct deterrence to EV adoption even in the compact city. Availability of charging opportunities, be it the public ones or home-based ones, plays a critical role in an individual's decision of EV adoption in the high-density city. Our study reveals a strong disparity in the pathways towards EV adoption between the Asian and the Nordic contexts, thereby illustrating the necessity to differentiate e-mobility promotion policies in compact cities from low-density cities.

Refereed/Peer-reviewed With the proliferation of electric vehicles (EVs), EV charging cost will become an integral part of household energy cost. This research proposes a novel household energy cost optimisation method for grid-connected homes with EV under power export constraints. It addresses the limitations of previous studies by incorporating more realistic variable EV charging characteristics, power export limits, degradation of battery energy storage (BES) and battery salvage revenue into a comprehensive techno-economic energy system model. Cost optimisation results are presented for four system configurations using relatively new time-of-use (ToU) tariff and real load and photovoltaic (PV) generation data for South Australian households. Sensitivity analysis for annual energy cost (AEC) is conducted by varying daily household load demand, PV/BES capacity, power export limits and PV/BES cost. Power flow and peak demand analyses are presented to the impacts of PV, BES and EV on household demand. Results show that PV with BES and EV is the most economical configuration for individual households, where the AEC can be reduced by up to 39.6% compared to a normal household without EV, PV and BES. The BES can effectively reduce household power and energy demands during peak periods by up to 80.4% and 89.1%, respectively.

Electrification of bus fleets in most cities is expected to rise due to its significant environmental benefits. However, electric buses have limited driving range and long recharging times. Additionally, electric buses require special charging infrastructure, which overall makes them less flexible than conventional diesel buses. Due to the limitations of the electric bus technologies, further adjustments have to be made to the current bus transport planning problems. The scheduling of electric vehicles is recognized as a fast-growing area of research. In this paper, we review 43 articles related to the electric bus technologies and give an overview of the different problems in the electric bus planning process (strategic, tactical and operational). The different problems are: 1) investment of electric bus fleet and charging infrastructure, 2) placement of charging infrastructure, 3) the electric vehicle scheduling problem (E-VSP) and 4) the charging scheduling problem. Given a set of timetabled trips and recharging stations, the E-VSP is concerned with finding a vehicle schedule that covers the trips and satisfies the driving range and recharging requirements of electric buses while minimizing operational cost. A detailed literature review of the constraints associated with the E-VSP and the solution approaches proposed to solve it is given. Rescheduling aspects or considerations of robustness for scheduling of electric vehicles is identified as a future area of research. Furthermore, integrated electric bus planning is considered as a crucial area of research and integrated approaches could further improve the efficiency of electric bus transport systems.

We consider a general electric vehicle (EV) charging system with stochastic demand, demand request locations, and predetermined charging facilities (including charging station locations and charger capacities). The objective is to design a good routing strategy that accommodates well demand-request dynamics so as to satisfy the charging system’s stability constraints and also minimize the EV’s mean response time. We introduce a class of flexible and measurement-based routing policies called “partition-based random routing” (PBRR) and show that the performance measure of interest can be formulated as a constrained optimization problem with a convex objective function when the system is heavily loaded. This formulation enables us to establish strong theoretical results that are in aid of finding the optimal routing solution; however, in practice, finding this solution requires rather involved numerical calculations. To that end, we propose a surrogate, easy to design and implement, optimization algorithm for finding the desired optimal routing solution. Numerical work based on synthetic data shows that the performance of the developed routing strategy and its fast implementation is highly satisfactory for a number of system settings.

Masses of user-side smart meter data provide a foundation for utilities to acquire users’ electricity consumption patterns and promote refined energy management. Therefore, the charging load of electric vehicles can be identified non-intrusively through the smart meter data of residential users, which provides flexible resources for the demand side response of power grid enterprises and is beneficial to the power dispatching decision making. Meanwhile, revealing residential energy consumption behavior from smart meter data is treated as energy privacy leakage. Extensive data collection from smart meters has raised privacy risk, which could lead to the reduction of trust in utilities from households. Aiming at this problem, the paper proposes a non-intrusive method to extract residential electric vehicles, and on this basis, discusses the privacy protection. In this paper, DWT method is used to decompose the total power of smart meter to extract the charging load of electric vehicle, and similar charging load patterns are added to smart meter data to preserve the residential EV charging behavior privacy. Practical households with EVs are used to test the proposed method and the result shows its feasibility for the performance of privacy-preserving.

This paper describes a technical feasibility study of on-board metal hydride storage systems. The main advantages of these systems would be that of being able to replace counterweights with the weight of the storage system and using the heat emissions of fuel cells for energy, making forklifts a perfect use case. The main challenge is designing a system that supplies the required energy for a sufficiently long period. A first draft was set up and analyzed to provide a forklift based on a fuel cell with hydrogen from HydralloyC5 or FeTiMn. The primary design parameter was the required amount of stored hydrogen, which should provide energy equal to the energy capacity of a battery in an electric vehicle. To account for highly dynamic system requirements, the reactor design was optimized such that the storage was charged in a short time. Additionally, we investigated a system in which a fixed amount of hydrogen energy was required. For this purpose, we used a validated simulation model for the design concepts of metal hydride storage systems. The model includes all relevant terms and parameters to describe processes inside the system's particular reactions and the thermal conduction due to heat exchangers. We introduce an embedded fuel cell model to calculate the demand for hydrogen for a given power level. The resulting calculations provide the required time for charging or a full charge depending on the tank's diameter and, therefore, the necessary number of tanks. We conclude that the desired hydrogen supply times are given for some of the use cases. Accordingly, the simulated results suggest that using a metal hydride system could be highly practical in forklifts.

Automotive air conditioning heat pump systems draw increasing attention as an alternative to conventional electric heaters. Once the air temperature falls below the freezing point, frost begins to form on the surface of the evaporator. The accurate defrost strategies could avoid heat exchange efficiency reduction and prevent heating capacity from decaying, which are the main factors affecting the energy efficiency of electric vehicle heat pump systems. The frost characteristic on micro-channel heat exchanger of the electric vehicle heat pump system is tested, and a novel defrosting strategy which is based on the dimensionless analysis method is introduced too. Conclusions could be drawn as following: a correlation that considers the effects of ambient parameters and structural parameters has been developed to predict the frost thickness on fins with dimensionless analyses. Based on this correlation, the relative error and the RMS error between the experimental data and the predicted value are 8.44% and 12.54%, respectively; the effective blockage ratio (EBR), a parameter that accurately reflects the blockage ratio and directly linked to the system operation state, is first proposed. Through the analysis between the EBR and system heating capacity, it can be concluded that the optimal defrosting start-point is identified as the EBR reaches 30%. With this method, the defrosting start-time can be found accurately and effectively to avoid unnecessary defrost cycles, thus leading to a highly efficient operation and low energy consumption for the electric vehicle heat pump system.

Motivated by the worldwide promotion of green transportation, this study aims to determine the optimal location of multi-type electric vehicle (EV) charging stations, e.g., fast and slow charging stations, for maximizing the covered traffic flows under a limited budget while considering EV users' partial charging behavior and elastic demand. A two-phase approach is proposed to efficiently solve this problem. The efficacy of the proposed two-phase approach is demonstrated by numerical experiments on the highway network of Zhejiang Province, China, and policies towards promoting transport electrification are discussed from various aspects. In particular, our policy suggestions are fivefold: (a) how to save investment in station construction by understanding EV users’ tolerance for travel cost deviation; (b) how to determine the budget policy to ensure an efficient utilization of the investment; (c) how to optimally select the location and type of charging stations; (d) how to devise the operation regulations based on the workload of each station; as well as (e) how to plan the station construction in a mid- or long-term implementation. We also discuss some prospective challenges and opportunities regarding charging station construction and operations in the context of continuous innovation in energy and communication technologies such as the Internet of Things (IoT).

14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS), Casablanca, MOROCCO, JUN 29-JUL 01, 2022; International audience; In electric vehicles (EV), control strategies play an important role in energy management and disturbance rejection. The studied electric vehicle traction chain in this article consists of a permanent-magnet synchronous motor (PMSM), an inverter and transmission. The proposed robust field oriented control strategy is based on integral sliding mode concept with exponential reaching law (ISMC-ERL). Unlike the conventional control techniques that lead to chattering effect, the adopted control scheme allows avoiding this drawback and ensuring a high performances, the ISMC-ERL allows also achieving high control performance in case of appearance of external load and parameter variations. Computer simulation using Matlab/Simulink environment have been carried out to evaluate the performance and the consistency of the proposed control approach. Copyright (C) 2022 The Authors.

This paper presents the influence of wire electrical discharge machine (WEDM) cutting parameters on the magnetization characteristics of electrical steel. The demand for electrical steel keeps increases because of the high demand of electrical motor and transformers for electric vehicle and smart grid applications. The magnetization characteristics of the laminated steel used in these machines have dominates the performance of these machines. The cuttings methods and cutting parameters have huge role in deciding the magnetization characteristics. In this work, WEDM is used to cut the electrical steel in to small pieces. The cutting parameters such as current, feed rate and on-time of WEDM are varied by keeping two of these parameters fixed and one varied. From the electrical steel sheet, 18 sample small electrical steel pieces are obtained at various cutting parameters of WEDM. The magnetic field – magnetization characteristics are obtained using vibrating sample magnetometer (VSM). This analysis shows that higher current, lower on-time and higher feed rate is providing higher magnetization.

This paper aims to integrate Industry 4.0 technologies, precisely the Digital Twin concept and smart materials, with the Electric Vehicle (EV) industry, to complement the adoption of Electric Vehicles (EVs) in the Indian market. In the current scenario, the adoption of EVs in India faces challenges ranging from insufficient charging facilities to the difficulties related to the complicated connections inside an EV, making the repair and maintenance of these vehicles quite arduous and costly. This paper specifically deals with the complexities regarding the repair and maintenance of EVs and aims to ease the process to ensure a bright future for the EV industry in the Indian markets. Throughout this paper, different challenges regarding the repair and maintenance of EVs are discussed. The solutions to overcome these challenges with the help of various Industry 4.0 technologies and smart materials will pave the way for the smooth and efficient adoption of EVs in the Indian markets. The paper also discusses the significant research challenges associated with the study and its future scope in the industry. The findings of this study enabled us to recognize a variety of smart materials that the EV industry can use in their cars which will make the maintenance of EVs much more affordable by cutting down the heavy costs incurred in getting the dents and scratches repaired if the vehicle meets an accident. Also, the use of Digital Twin in the servicing and repair of EVs will make the process a lot more convenient by reducing the service time and related costs drastically. Further in our study, we have described in detail the working process of DT and smart materials in the EV industry to achieve the goals of this study.

The performance of any Electric vehicle depends upon the Power Battery used in the vehicle. Both very high as well as low temperatures can have a negative impact on the efficiency of the battery. Operation of a battery at a high temperature may lead to overheating or even lead to an explosion of the battery which has been witnessed in lots of cases. If a battery operates at a lower temperature then also it might be complicated for the batteries to work efficiently, So this paper focuses on the Thermal management system of Lithium-ion batteries. There are different methods and ways studied by previous researchers by which rise in the temperature in the batteries could be balanced. A phase change material (PCM) having properties of copper foam/paraffin was used in the battery pack to get the optimum temperature for the battery module. Phase change materials have a lower value of thermal conductivity so in this study copper foam was used which has a better thermal conductivity value i.e 0.21 W/m-K. Solidus and liquidus temperature also play a major role in determining the temperature rise. The rise in temperature in the battery module depends on how the cell inside the modules is arranged. It also depends on the shape and size of the cells used. This paper focuses on getting the most accurate arrangement of the cells to get the most efficient battery module.

Recently, researchers have devoted more attention to supercapacitors (SCs) to integrate with batteries in energy storage systems (ESSs) for vehicle applications. In this study, we attempted to characterize the use of SCs in the ESS for a PEM fuel cell vehicle equipped with an alternator to maximize the performance of regenerative braking. We applied lithium-ion batteries (LIBs) and SCs as energy storage devices to examine their effect on ESS. Then we used a hysteresis brake to apply controllable braking force on the flywheel to form hybrid braking (HB) and made efforts to study its behavior to suggest a braking control strategy. We also ran the whole system over the rotational speed to cover the range of driving speed. At last, we sized the SCs for the most commonly used fuel cell electric vehicle (FCEV) in Korea, i.e., Hyundai NEXO, based on the results obtained from the above study by alternator efficiencies.

At present, the fuel cell is playing major role in the current automotive industry application. Also, the fuel cell have the features of good reliability, high flexibility, reduced demand for foreign oil, and enhanced environment quality. However, for each and every functioning temperature variation, the Fuel Cell (FC) gives different working peak power points on it nonlinear voltage versus current curves. In this article, an Improved Differential Evolutionary Optimization (DEO) method is included in the Fuzzy Logic Controller (FLC) to enhance the maximum power delivered by the fuel cell stack. Here, the main objective of the DEO technique is to optimize the membership functions of the fuel cell stack input and output variables. Due to the proper selection of membership functions, the proposed MPPT controller gives fast tracing speed, good accuracy, and high sustainability. Also, a high voltage gain, nonisolated dc-dc converter is proposed in this work to enhance the fuel cell output voltage and reduce the current flowing through the consumer loads. The properties of converter are less potential pressure across the switches, extensive output voltage functioning, low supply current ripples, and high supply voltage withstand capability.

This work illustrates a detailed circuit analysis and related simulation of dual active bridge bidirectional converter. The dual active bidirectional converter is used in many industrial applications such as hybrid electric vehicle, interfacing energy storage devices on distributed generation system etc. Detailed circuit analysis of the converter is one of the most challenging work and this paper addresses the same with adequate analysis and simulation results.

Electric vehicle (EV) are the future of the automobile industry in terms of reducing the greenhouse gas emissions, air pollution and the better life comfort level all over the world. This paper compares the results obtained by various authors in terms of life cycle assessment (LCA) of EV and conventional vehicles powered by fossil fuels, and the life cycle cost (LCC) analysis of the both types of vehicles, as every new technology comes with an additional cost which need to be feasible with respect to the present trends. And also discuss some of the software used for both type of LCA and LCC analysis. The finding of review concludes that with the adoption of EV there is a reduction in greenhouse gas emissions (GHG) but there is an increase in the human toxicity level due to the larger use of metals, chemicals and energy for the production of powertrain, and high voltage batteries. And in terms of cost it has flexible pricing as there is uncertainty in pricing of future gasoline and electricity mix, higher initial cost at the time of purchasing due to higher pricing of battery.

The fuel cell power generation systems are using in most of the present hybrid Electric Vehicle (EV) technology. The output characteristics of fuel cell arenonlinear. Also, the fuel operating point fluctuate continuously based on its operating temperature condition. In order to track and stabilize the operating point fuel cell, an Artificial Neuro Fuzzy Inference System-Genetic Algorithm Optimization (ANFIS-GAO) method is used. The merits of this proposed Maximum Power Point Tracking Technique (MPPT) are high extraction power capacity, high reliability, less oscillations, and fast tracking speed. The fuel cell is having a drawback of high output current and its less output voltage generation capability. To overcome this issue, in this work, an interleaved dc-dc converter circuit is used to enhance the fuel cell energy. The advantages of the proposed dc-dc converter are low voltage stress across the switches, less input current ripples, faster response, reduced electromagnetic emission, and high reliability. The proposed hybrid power point tracking technique performance is evaluated against with the Variable Step-Perturb & Observe (VS-P&O) algorithm. The MATLAB/Simulink environment is used for the performance analysis of proposed system.

Several energy management strategies, of increasing complexity, are compared in term of cost benefit for a Photovoltaic /battery system providing electricity to an accommodation building with an electric vehicle. Rule based control methods with or without production forecasting are implemented and compared to a linear programming strategy used as a reference. The improvement in term of gain between simplest and reference methods is about 27%. It appears that the battery cycles number differs greatly (up to 55%), leading to a more or less rapid ageing. Battery degradation models are thus added and a corresponding cost is introduced in the strategy benefit. The results, depending on the initial battery cost, are significantly impacted, changing the relevance of the control strategies.

This paper proposes a distributed energy management for active distribution network (ADN) considering aggregated office buildings. It aims to dispatch the flexible resources of office buildings to actively participate in the optimization of the ADN. Firstly, by considering the comfortable indoor temperature range and travel habits of workers, an optimal operation model for the office building with integrated heating ventilation and air conditioning (HVAC) systems and electric vehicle (EV) charging piles is constructed. Secondly, based on the piecewise linearization and second-order cone relaxation (SOCR), an ADN optimization model with aggregated office buildings is formulated and converted into a mixed-integer second-order cone programming (MISOCP) model. Then, considering the private information protection, the alternating direction method of multipliers (ADMM) is used to solve the ADN optimization model to realize the distributed energy management of the ADN with aggregated office buildings. Finally, the scheduling results of the ADN optimization model under different comfortable indoor temperatures and control methods of EVs are analyzed in the winter heating scenario. Numerical results show that the proposed method can increase the penetration of distributed photovoltaic (PV) generation and decrease losses of the distribution network while ensuring the temperature and travel demands of workers in office buildings. The information privacy of each district can also be protected with the proposed method.

As electric vehicles (EVs) are eco-friendly and have the feature of flexible power storage, the electric vehicle development is strongly supported by many governments. The market share of EVs has been rising steadily in recent years to promote the long-term growth of the available EV battery resources to participate in vehicle to grid (V2G). When a large number of electric vehicles can connect to grids randomly for charging or discharging, this inevitably brings large load fluctuation in regions and new challenges to the operation scheduling and control of power systems. Therefore, this paper proposed a new multi-level optimal V2G scheduling approach, ensuring the smooth operation and control from the V2G control center to the EV users. This paper also introduced a new EV economic dispatch optimization model to minimize the operating costs of regional V2G systems. A case analysis of the IEEE 33-bus system with 100 EVs verified the feasibility of the proposed model and indicated that the proper size control of total EV battery capacities with ramp adjustment can reduce additional load fluctuations caused by large-scale vehicles to grid. Moreover, the multi-stage TOU pricing mode is another effective measures that can trigger a next round of EV charging and discharging service.

This work primarily focuses on electrical characteristics of a hybrid power system (HPS) incorporating renewable energy generation (REG) (HPSREG). The major components of HPSREG are the resources coordinated with multi-unit of photovoltaic cells, multi-unit of wind turbine generators, a diesel engine generator (DEG), energy storage system (ESS) with diverse nature and an electric vehicle (EV). The performance characteristics of HPSREG are determined by constant generation of power from the various sources as well as varying load perturbations. As the variation in load demand will introduce fluctuation in frequency and power with constant generation. In few of overcome the frequency and power deviation under both the above-mentioned generation and load demand conditions, proper control technique is required. In order to control the deviation in frequency and power, an integration in the environment of fractional order (FO) calculus for proportional–integral–derivative (PID) controller and fuzzy controller, termed with FO-Fuzzy PID controller tuned with quasi-opposition based harmonic search (QOHS) algorithm has been proposed. The results acquired with the proposed FO-Fuzzy-PID controller are then analyzed along with FO-PID and PID controller route for quantify effectiveness for the same under the considered cases to determine the effectiveness of the algorithm undertaken. Sensitivity investigation is also conducted in order to show the strength of the technique under study of differences in HPSREG parameters of magnitude.

The dynamic wireless charging system (DWCS) is developed to solve the problems of large battery volume and mileage anxiety of electric vehicles. However, the accurate position detection for electric vehicle DWCS is facing challenge. The traditional communication, detection and estimation methods are difficult to accurately obtain the position. To tackle this problem, the visual inertial simultaneous localization and mapping (VI-SLAM) method is applied to the electric vehicles DWCS. Firstly, the graph optimization based tight coupling method is used to integrate the monocular visual and inertial measurement unit (IMU) measurements. Secondly, the NVIDIA TX2 and MYNT VI-sensor suite are assembled, which the MTi300-IMU is treated as the ground truth system. Finally, the mobile vehicle is controlled to race on the simulated DWCS pathway. The experimental result shows that the method achieves great performance with the accuracy of centimeter level. In particular, the root mean square error (RMSE) of pose (i.e. position and orientation) in X, Y, Z directions are 0.086 m, 0.092 m, 0.102 m and 2.423°, 1.682°, 2.501°, respectively. Compared with the smooth variable structure filter (SVSF) based SLAM method, 0.106 m, 0.098 m, 0.130 m and 3.069°, 3.261°, 2.961°, the accuracy of ours is increased by 18.87%, 6.12%, 21.54% and 21.05%, 45.42%, 15.54%, respectively.

Under the effects of internal factors and the external environment, some customer requirements can be extremely sensitive to change; these are known as sensitive requirements. These requirements drive the identification and design of the relevant engineering characteristics. This study defines such characteristics as agile engineering characteristics (AECs), which are crucial for redesigning products and prolonging their service life but attract limited research and practice attention. The research question of identifying the product AECs is answered in this study, and three key issues are solved in the proposed methodology. First, the contribution of product engineering characteristics to customer requirements is calculated using an orthogonal experimental design, and the optimal level combination of engineering characteristics to satisfy the customer requirements is determined through the analysis of variation. Subsequently, the relative importance of engineering characteristics is calculated based on data envelopment analysis. Next, an agility index is defined by integrating the customer satisfaction degree, the relative importance degree of engineering characteristics, and the variation of the level of engineering characteristics, to identify the AECs. Finally, based on the identification of the AECs of an electric vehicle air-conditioning heat pump scroll compressor, the results are analysed and compared to illustrate the feasibility and effectiveness of the proposed method.

Representative driving cycles and specific vehicle types play an important role in analyzing energy consumption to provide valuable references for manufacturers and government inspections. In this study, a parallel plug-in hybrid electric vehicle with different driven configurations is employed to analyze energy consumption under a typical driving cycle. Specifically, a typical driving cycle of Tianjin is constructed by integrating the clustering and Markov chain algorithms. Considering the various layouts of energy systems in plug-in hybrid electric vehicles, three different driven configurations (P2, P3, P4) of the parallel plug-in hybrid electric vehicle are established. Finally, the energy consumptions of the different configurations are analyzed under the proposed typical driving cycle and the compared China Automotive Test Cycle. Results of two driving cycles demonstrate that the fuel consumption of the P3 configuration is lower than that of the P2 and P4 configurations, respectively, while the purely electric driving range of the P3 configuration is longer than the other two configurations, respectively. These results provide a basis for the formulation of China’s new light vehicle emission standards and reduce exhaust emission by making the vehicles more economically suitable for the road conditions in Tianjin.

This paper focuses on a novel model named multi-station fusion (MSF). The proposed model integrates transformer substation, data center, energy storage system (ESS), photovoltaic (PV), electric vehicle charging station (EVCS), connection information base station and other systems. It aims at improving the utilization rate of the spare space in existing substation, reducing operation and maintenance cost, and increasing the revenue of power grid enterprises. This paper designs three types of AC/DC system based on reliability priority, economy priority and retrofit flexibility priority. Markov process Monte Carlo method is introduced to verify data center reliability in MSF. The business model of MSF is analyzed. Finally, the construction scheme of MSF model, the contents of the planning considerations and cost–benefit study demonstrate the economic efficiency of the proposed model.

Uni- and bi-directional electric vehicle charging schedulers are key enablers in the mitigation of the negative impacts, such as peak powers, of electric vehicles and renewable energy sources on the grid. This paper presents two electric vehicle charging schedulers based on model predictive control algorithms for four different charging strategies to minimize electricity bills and peak powers for a local energy system. The schedulers use forecast values for photovoltaic production, load demand and electric vehicle state, from an existing database, a deep recurrent neural network and an electric vehicle battery model, respectively. The charging strategies are tested in a simulator that uses real and recent charging events from a charging location in Brussels. The results show the possibility to drastically reduce peak powers and with them, electricity bills as well, near real-time. Bi-directional charging shows the best results compared to uni-directional charging. These good performances are amplified by oversized PV systems but reduced with higher minimum state-of-charge.

As electric vehicle (EV) sales grew approximately 50% year-over-year, surpassing 3.2 million units in 2020, the "roaring era" of EV is around the corner. To meet the increasing demand for low cost and high energy density batteries, anode-free configuration, with no heavy and voluminous host material on the current collector, has been proposed and further investigated. Nevertheless, it always suffers from several nonnegligible "bottlenecks", such as fragile solid electrolyte interface, deteriorated cycling reversibility, and uncontrolled dendrite formation. Inspired by the "compensatory effect" of some disabled people with other specific functions strengthened to make up for their inconvenience, corresponding quasi-compensatory measures after anode removal, involving dimensional compensation, SEI robustness compensation, lithiophilicity compensation, and lithium source compensation, have been carried out and achieved significant battery performance enhancement. In this review, the chemistry, challenges, and rationally designed "quasi-compensatory effect" associated with anode-free lithium-ion battery are systematically discussed with several possible R&D directions that may aid, direct, or facilitate future research on lithium storage in anode-free configuration essentially emphasized.

Fuel cell hybrid electric vehicles (FCHEVs) are considered to be the most attractive long-term option for passenger cars. Several barriers, such as cost, durability and hydrogen refueling infrastructure, must be overcome for a wider use of FCHEVs. In this paper, a mid-sized FCHEV is modeled and simulated in ADVISOR to analyze the influence of hybridization factor on vehicle performance and costs. The results are compared with those of the Toyota Mirai in order to find the optimum size of the fuel cell stack and the number of battery modules that meet various driving requirements, minimize hydrogen consumption and vehicle cost. The best results are obtained by reducing the fuel cell stack power by 58%. A 7.7% increase in equivalent fuel economy (71.6 MPGe) and a reduction of 25% in the vehicle cost is achieved.

We study attitudes, perceptions, and valuations of a convenience sample of Chilean employees from an electric distribution company who applied for a subsidized electric vehicle (EV) acquisition program. The subsidy was randomly assigned among the interested applicants. We use this data in an experiment to assess the impact that being a user, or a non-user of an EV has on the factors under study, using focus groups and a stated preference (SP) experiment. In the focus groups, users mentioned relatively more benefits and barriers of EVs, while non-users spontaneously stated that a limited charging network at the urban level could be an issue. The SP survey suggested that being a user did not trigger a change in environmental attitudes; on the other hand, it significantly boosted perceptions of maintenance costs and driving range. Additionally, discrete choice models estimated from the SP data suggested a possibly null willingness to pay for urban charging infrastructure, expressed as a percentage of current gas stations, among the user group. We discuss possible policy implications that can be inferred from this analysis, considering the size and source limitations of the available sample.

This research utilizes a hybrid technology through resonant line-level control phase-shifted full-bridge (HR-LLC-PSFB) dc–dc converter. In this research, a line level control (LLC) auxiliary circuit is used to achieve zero-current soft-switching turned-off operation of a controlled switch. The zero-voltage soft-switching turned-on operation is achieved by using the transformers’ magnetic branch energy and the energy reflected by the output filter inductor. The effective turn’s ratio of the isolating transformers is used to reduce the inverter circuit’s voltage stresses. The effect of voltage overshoots and freewheeling circulating current in rectifying branches is improved by implementing an energy recovery circuit with an output filter. A closed loop coordination proportional integral derivative (PID) controller is proposed to generate the pulse width modulation (PWM) control signal to transmit smooth power by controlling output voltage and current values. The droop constant current charging (CCC) and reduced constant current charging (RCCC) technique is proposed for the charging process. Simulations performed in MATLAB and results substantiate the proposed work superiority in comparison to other topologies.

This paper introduces a new charging service mode that is emerging in the market for electric vehicles called the valet charging service. Through this service mode, a company provides free valet charging services to users that are far away from charging stations, and users can request the fee-based service when they are unwilling to charge electric vehicles themselves. The valet charging service is a new service mode that promises to alleviate users’ worries regarding the lack of nearby charging stations. However, whether such a mode truly benefits a specified system remains open to careful study. We build the charging station locating and sizing model with the valet charging service under demand uncertainty. We first formulate this problem as a two-stage stochastic mixed-integer model (TSMIP). Then, considering that the demand probability distribution is difficult to estimate, we reformulate TSMIP into a risk-averse two-stage stochastic mixed-integer model (RTSMIP) with the ambiguity set. Next, we propose an SAA-based hybrid-cut L-shaped algorithm to solve this model. By comparing the total cost of the system with and without the valet charging service under different scenarios through hypothetical numerical experiments and a realistic case study in Shanghai, meaningful insights are provided that include identifying the conditions in which the valet charging service works well and how it influences the location and capacity of charging stations. These insights provide some understanding of this new service mode that can help companies make rational decisions.

Due to the low output distortion and switch voltage stress, Vienna rectifiers have been widely utilized in high-power applications such as communication power supplies, wind power systems and electric vehicle charging piles. However, Vienna rectifiers still have to face tough challenges under unbalanced grid considering the inherit operating range limitation caused by its structure. Consequently, a deadbeat predictive power control (DBP-PC) for the Vienna rectifier under unbalanced grid is proposed. Using the positive and negative sequence double dq decoupling current control strategy, the operating range of the Vienna rectifier under unbalanced grid is analyzed. Furthermore, an improved DBP-PC for Vienna rectifiers under unbalanced grid is proposed. By the improved DBP-PC, the stable operating range can be effectively extended. Finally, the corresponding simulations and experiments are executed to demonstrate the performance of the proposed control strategy.

Nowadays, the energy management of multisource hybrid systems is becoming an interesting and challenging topic for many researchers. The judicious choice of the energy management strategy not only allows for the best distribution of energy between the different sources, but also reduces the system's consumption, increases the life span of the used sources and fulfills the energy demand that affects the autonomy of the electric vehicle (EV). A novel hybrid control strategy based on the interconnection and damping assignment passivity-based control (IDA-PBC) technique is proposed while considering the battery State of Charge (SOC) and the hydrogen level operating conditions. PBC is a very powerful nonlinear technique, which uses important system information such as the system energy information. The Artificial Neural Network (ANN) is used for defining the appropriate references for the proposed controller to properly share the load power demand among the sources. Consequently, the proposed nonlinear control enables dispatching the requested power/energy among sources under source limitations. The real time experimental results demonstrate the enhanced efficiency of the hybridized ANN together with the IDA-PBC control. This work proposes a complete study and solution, from modeling, control, stability proof, simulation to practical validation. New constraints are emerging in anticipation of the real-time use of FC hybrid systems. These constraints and objectives are mainly related to the limitations of energy resources and the minimization of hydrogen consumption. The supervision of hydrogen level and battery SOC resources are proposed by using ANN, which gives the battery current and/or SC set point to the control loops. Experimentation works have validated the feasibility of this optimization technique.

Establishing a reasonable energy management strategy (EMS) is the key to improve the service durability, power performance and fuel economy of the fuel cell hybrid electric vehicle (FCHEV). This paper obtains energy distribution optimal solution for the fuel cell hybrid bus (FCHB) based on Pontryagin's minimum principle (PMP) algorithm, and the problems of inaccurate estimation of motor power and difficult real-time application are solved. Firstly, the driving feature recognition is completed by collecting the motor output power directly when the FCHB stops at the station. On the basis of it, the sub-optimal co-state value is chosen. Secondly, the sub-optimal co-state is used to complete the real-time application of PMP algorithm in the driving segment. The results are acquired through the simulation and the actual comparison experiment, compared with rule-based simulation and rule-based actual experiment, the hydrogen consumption of the proposed strategy decreases by 20.3% and 28.9% on average. Moreover, the online computation time per step of the proposed strategy is 3.64 ms averagely, less than sampling time interval 1s. It is shown that the proposed method has lower hydrogen consumption rate and excellent real-time performance.

The rapid development of electric vehicles (EVs) makes the load of electric vehicle charging stations (EVCSs) affect the power grid. Aiming at the low accuracy of charging station load forecasting caused by the number of EVs, temperature and electricity price, and other factors, this paper proposes a load forecasting method of EVCSs based on a combination of multivariable residual correction grey model (EMGM) and long short-term memory (LSTM) network. Firstly, load influencing factors are analysed, and the grey theory is introduced into the load forecast of EVCSs. The role of EMGM in taking into account the effects of multiple factors and eliminating cumulative errors is analysed. Then, the EMGM and LSTM networks are combined to establish a mapping from the influencing factor data to the forecast, reducing the load forecast error of EVCs. Simulation and experimental results show that the accuracy of EVCSs’ load forecasting can be improved by this method.

This study focuses on driver-induced changes to vehicle energy and emissions performance by altering drivetrain configurations. It defines and examines the motivations for the use of drive modes in plug-in hybrid electric vehicles (PHEVs). Drive modes are defined as user-selectable drivetrain configurations that allow drivers to change vehicle performance aspects such as altering its propulsion status from electric to gas, changing the steering responsiveness, suspension stiffness, and regenerative braking strength. Despite the direct impact they have on the vehicle’s energy usage, greenhouse gas emissions, and local air emissions, the impact of drive modes are not traditionally measured as part of the vehicle’s performance and certification tests. The impact of drive mode choice can explain a portion of the discrepancy between PHEVs expected and on road performance as a recent study by the authors found that the use of certain modes can result in a 15–30% increase in total energy usage in a vehicle traveling at freeway speeds. This is the first study to examine the motivations for the use of these drive modes and provides an overview of PHEV drive modes, classifying them into two main mode categories (propulsion adjustment and driver experience) and eight different mode types based on their intended purpose. This is followed by a logistic regression of 11 indicators for mode usage taken from a survey of over 4,500 PHEV owning households, showing that gender and the number of long-distance trips were the most commonly associated with usage across mode types.

The hydrogen proton exchange membrane (PEM) fuel cells are promising to utilize fuel cells in electric vehicle (EV) applications. However, hydrogen PEM fuel cells are still encountering challenges regarding their functionality and degradation mechanism. Therefore, this paper aims to study the performance of a 3.2 kW hydrogen PEM fuel cell under accelerated operation conditions, including varying fuel pressure at a level of 0.1–0.5 bar, variable loading, and short-circuit contingencies. We will also present the results on the degradation estimation mechanism of four fuel cells working at different operational conditions, including high-to-low voltage range and high-to-low temperature variations. These experiments examine over 180 days of continuous fuel cell working cycle. We have observed that the drop in the fuel cells' efficiency is at around 7.2% when varying the stack voltage and up to 14.7% when the fuel cell's temperature is not controlled and remained at 95 °C.

Environmental issue such as global warming caused serious natural disasters such as flood and drought due to CO2 emission. Global countries cooperate to address the global warming represented by Paris Agreement, which encouraged actions to restrict CO2 emission and global average temperature increase. H2 has been received more attention than before due to its eco-friendly property. Fuel cell and fuel cell electric vehicle (FCEV) are the widely used H2 application in terms of energy and transportation sector. However, most of the researches were carried out related to H2 supply chain. In hence, in this study, economic analysis considering total cost of ownership (TCO) of FCEV was conducted and correlation between TCO and market share of FCEV was figured out by drawing a regression curve. Finally, an optimization model was developed to obtain the optimal H2 fueling station arrangement model in case of 2022, 2030, and 2040 depending on learning rate (8, 13, and 18%). The $ 46,444.2 of TCO was computed considering three years of ownership length. In addition, 4.3, 31.7, and 94.0% of maker share in 2040 were reached in case of 8, 13, and 18% of learning rate, respectively. Finally, it was unveiled that H2 fueling station is preferred to construct equally in nation and the construction region will be shifted to a higher population density region as time passed.

With technical challenges and great opportunities, aqueous metal-sulfur batteries (AMSBs) are born in response to the great demand of growing electric vehicle industry. In this paper, the quantitative analysis on thermodynamic calculations of 316 systems is performed for AMSBs and 63 Eh-pH diagrams (Pourbaix-diagram) as the qualitative references are also presented, aiming to confirm the certainly technical barriers, to illustrate the working mechanisms, and to choose promising battery systems. On the basis of thermodynamic calculations, energy storage mechanism, energy density, standard electrode potential, specific capacity, volume changes of electrode materials, chemical stability of metal anodes, and chemical stability of discharge products in AMSBs are analyzed and discussed systematically. In addition, security risks, kinetic issues, and practical applications are discussed and the challenges from aqueous electrolyte are also analyzed to introduce AMSBs more comprehensively. Finally, some promising metal-sulfur systems are proposed according to the joint analysis of thermodynamic calculations and practical conditions.

Minibus taxi public transport is a seemingly chaotic phenomenon in the developing cities of the Global South with unique mobility and operational characteristics. Eventually this ubiquitous fleet of minibus taxis is expected to transition to electric vehicles, which will result in an additional energy burden on Africa's already fragile electrical grids. This paper examines the electrical energy demands of this possible evolution, and presents a generic simulation environment to assess the grid impact and charging opportunities. We used GPS tracking and spatio-temporal data to assess the energy requirements of nine electric minibus taxis as well as the informal and formal stops at which the taxis can recharge. Given the region's abundant sunshine, we modelled a grid-connected solar photovoltaic charging system to determine how effectively PV may be used to offset the additional burden on the electrical grid. The mean energy demand of the taxis was 213kWh/d, resulting in an average efficiency of 0.93kWh/km. The stopping time across taxis, a proxy for charging opportunity, ranged from 7.7 h/d to 10.6 h/d. The energy supplied per surface area of PV to offset the charging load of a taxi while stopping, ranged from 0.38 to 0.90kWh/m2 per day. Our simulator, which is publicly available, and the results will allow traffic planners and grid operators to assess and plan for looming electric vehicle roll-outs.

The increasing popularity of electric vehicles (EVs) leads to heightened demand for the charging infrastructure. More and more EV drivers install private charge posts, which can now be shared with others through certain mobile apps. This emerging phenomenon is becoming a prominent part of the sharing economy. To examine the impacts of post sharing on EV charging market, this study establishes game theory models on consumer choices among private, public, and shared options. Such peer-to-peer sharing and collaborative consumption redistribute the installation and operation costs of private charge posts in proportion to their increased utilization. Numerical analyses suggest that the sharing mode provides a win-win solution for charge post owners and non-owner consumers, as well as electricity distributors and public charging infrastructure operators. In the case of China, the estimated saving for charge post owners is between 20% and 50%, which can be translated into more non-government investment in the EV industry chain. The findings provide supporting evidence for policy-makers to promote private charge post sharing, especially with certain consumer subsidization at a reasonable level.

The sharp inclination in the emissions from conventional vehicles contribute to a significant increase in environmental issues, besides the energy crises and low conversion efficiency leads to the evolution of electric vehicles (EV). Hybrid electric vehicles (HEV) have efficient fuel economy and reduce the overall running cost, but the ultimate goal is to shift completely to the pure electric vehicle. Despite this, the main obstruction of HEV is energy storage capability. An EV requires high specific power (W/kg) and high specific energy (W·h/kg) to increase the distance travelled and reduce the time required for charging. The main focus of this paper is on the energy sources as these are the main components in EVs towards making them eco-friendly and cost-effective. Various topologies of EV technology such as HEVs, plug-in HEVs, and many more have been discussed. These topologies of EVs are based on the diverse combination of batteries, fuel cells, super-capacitor, flywheels, regenerative braking systems, which are used as energy sources and energy storage devices.

It is a harsh fact that the introduction of various government schemes to push electric vehicle (EV) utilisation does not seem to appeal to the consumers. There are a few barriers that prevent consumers from purchasing EVs. Thus, in the present study, we have tried to identify and analyse the prominent barriers to the adoption of EVs by scrutinising the existing literature and defining new barriers. From the literature review, 35 barriers have been initially identified in the context of the Indian market. The study uses the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach for its analysis. The prominence and causal relationship analyses indicate that familial factors that hinder the decision to buy an EV and unclear government policies regarding EVs were the primary concerns with regard to these vehicles getting traction in the market. The results of this study illustrate the causal relationships amongst the identified barriers. On the other hand, the study reveals that the pricing of the EVs is not a major issue and that the consumers are more concerned about the availability of maintenance support post purchasing of the vehicles.

Although the significant effect of heterogeneous green consumers on green innovation in electric vehicle firms and charging pile firms has been recognized, the influence of heterogeneous green consumers is still unclear. Therefore, we propose a two-dimensional model of vertically differentiated product preferences and horizontally differentiated green quality preferences. In the model, according to the heterogeneity in the perception of green awareness and anticipation of the green quality of charging pile firms, three green consumer segments, namely, light, medium and heavy, are considered. We use this economic model to examine the implications of green innovation by analyzing three green consumer innovation strategies. The findings show that when electric vehicle firms unilaterally implement green product innovation, the corresponding optimal price for charging pile firms decreases in the green consumer segment. The result also suggests that although three green consumer segments exist, it is wise for both types of firms to perform green product innovation. Furthermore, electric vehicle and charging pile firms should increase efforts in green innovation to satisfy consumers’ diversified green demands.

Inadequate consumer adoption has become a major obstacle to the diffusion of electric vehicles. Understanding and interfering with decision-making of these consumers may be the key to the acceptance of electric vehicles. In this study, an improved agent-based modelling is proposed to simulate the market acceptance of electric vehicles under multi-policy scenarios. The logic of consumer decision-making is portrayed by integrating consumer preferences, charging facilities, and the impact of government subsidies. The results show that the current consumers prefer plug-in hybrid electric vehicles and their attitude towards electric vehicles is more negative. A hybrid policy consisting of government subsidies and charging facility construction is the most effective strategy, where subsidies help stabilize consumption and charging facility construction facilitates the rapid electrification of vehicles in the current market. Moreover, increasing demand for sustainable consumption requires continuous upgrading of EV production technology. Based on these results, policy implications and suggestions for future electric vehicle industry are discussed.