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This report assesses the current and near-future conditions related to the disposal of Battery Electric Buses, specifically their batteries. The report begins by explaining the reasons and legislation in the current day which is pushing the adoption of zero emission buses as well as the growing interest in exploring new reuse and recycling methods. The subsequent section consists of interview responses from various transit agencies currently planning on electrifying their fleet, cataloging their concerns and options for disposal that are currently being considered. Following is a section with interview content from bus manufacturers on the current state of battery design and policies, as well as an exploration of future services possibly offered by these companies. The report then focuses on summarizing general trends in the US battery recycling and reuse market, as well as resources for transit agencies, to determine which disposal strategy is right for them. Finally, the report closes with recommendations for all stakeholders on how to best prepare for future disposal needs, from both the manufacturer and transit agency perspective, and some recommendations to increase battery recycling/reuse capacity domestically addressed to the state and the federal government.

California maintains ambitious clean vehicle targets, including a mandate that all passenger vehicles sold in the state be zero-emission by 2035. This paper examines the distributional impacts of six light-duty clean vehicle incentive programs, especially for equity. The authors find that of the more than $1.9 billion allocated, only about $311 million was distributed to households in disadvantaged communities (DACs). The Clean Vehicle Rebate Project and other statewide programs are heavily skewed towards benefitting non-DACs (87.9%). Clean Cars for All and other regional programs have been more effective at delivering funds to DACs (51%). Between 2015 and 2021, nearly every region of the state has increased clean vehicle registration rates, though rural areas and the Los Angeles core remain persistently low. In addition to spatial inequalities, there are disproportionately low clean vehicle registrations among lower-income areas. By projecting current and optimistic rates of adoption to 2035, the researchers found that California is unlikely to reach its goals, and marginalized communities will continue to be far behind in their adoption rates. Policy recommendations include: 1) allocate more funding for equity-focused clean vehicle programs, 2) be creative in maximizing used vehicle inventory, and 3) focus more on delivering rather than merely advertising the benefits of one-stop shops for incentive access.

Battery fault diagnosis is a key problem to ensure the safe operation of electric vehicles. In order to achieve accurate voltage prediction during the real operation of electric vehicles, this study proposed an electric vehicle real voltage prediction algorithm based on PCC-CNN-LSTM. First, the input and output characteristics were determined by Pearson correlation coefficient method. Considering the sampling time of individual cells in different battery packs, the number of cells and the working range of voltage, the parameters of the algorithm model were determined. Then, the sequential features in the voltage data were mined by CNN for feature extraction and input into LSTM for model training, which effectively solves the problem that the LSTM input feature dimension is too large to grasp the effective information, which makes the prediction accuracy decrease. Through the real vehicle test, for different types of battery packs, the evaluation indexes of the CNN-LSTM-based model are superior to those of other algorithm models (CNN, LSTM, GRU, and CNN-GRU). Finally, the superiority, reliability, and robustness of the proposed algorithm are verified by comparing with other algorithm models. [ABSTRACT FROM AUTHOR]

In a global context characterized by climate warming, the transport sector has found the use of electric vehicles to be one of the possible measures of decarbonization. Although the purchase rate of this type of vehicle is still low, there are many research fields related to both the development of the electric charging network and the improvement of batteries to ensure features that meet the expectations of users. Moreover, the increase of the use of electricity can cause issues in electrical network stability, especially during the peak hours. Therefore, this sector is facing new challenges, including the case of vehicle-to-grid (V2G), which is a solution that allows the use of vehicle batteries, not only as a source of energy for the vehicles, but also as stabilizers of the supply network when the vehicles are parked (i.e., no energy is needed for their activity). In the recent years, the researchers mainly focused on the energy infrastructure and technologies, neglecting problems related to the identification of the best locations for V2G services and the potential acceptance of the electric vehicles' owners, as well as on the potential energy that can be transferred to the grid according to the users' needs (e.g., to continue to use their vehicle for completing the daily activities). This paper proposes a methodology aimed at identifying potential areas for deploying V2G services by using floating car data (FCD) and at estimating the potential energy to be transferred to the grid without interfering with the daily activities. This methodology is finally applied to a case study of five provinces of the Veneto region, showing the significant results obtained. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid approach, termed ZOA‐SNN, for fault detection and identification in converter‐interfaced microgrids. By integrating the Zebra Optimization Algorithm (ZOA) with Spiking Neural Network (SNN) technology, the proposed method provides a comprehensive solution suitable for both grid‐connected and autonomous microgrid operation scenarios. The technique effectively isolates faults in the microgrid while maintaining operation continuity, particularly in islanded conditions. When operating in grid‐connected mode, distributed generators (DGs) provide electricity as needed. When the grid is not available, power sharing amongst DGs is controlled by voltage angle droop control. By isolating malfunctioning portions, the proposed protection system reduces load shedding, while DG control guarantees smooth islanding and resynchronization. Evaluation on the MATLAB platform demonstrates the superior performance of the proposed technique compared to existing algorithms such as Augmented Lagrangian Particle Swarm Optimization (ALPSO), Graph Convolutional Network (GCN), and Buffalo Optimization (BO). With an accuracy, recall, precision, and F1‐score reaching 98.5%, 99.2%, 99.1%, and 99.1%, respectively, the ZOA‐SNN approach excels in fault detection and classification. Additionally, it significantly reduces computation times for parameter calculation, enhancing efficiency in microgrid control systems. These results highlight the innovation and advantages of the ZOA‐SNN approach in enhancing the reliability and efficiency of fault detection systems in microgrid environments. [ABSTRACT FROM AUTHOR]

In-wheel motor (IWM) bearings are key components for load and motion transfer and are highly integrated into wheels. The heat issues of IWM bearings are directly related to the reliability and safety of IWMs and vehicles. This paper focuses on examining the bearings of a 15 kW IWM. The corresponding thermal analysis model is established after determining the heat source of IWM electromagnetic loss and bearing mechanical loss. The established model's validity has been confirmed through temperature experiments on the in-wheel motor. Subsequently, according to the influence rules of different heat sources on bearing temperature rise, it is found that the effect of electromagnetic loss on bearing temperature rise is greater than that of friction loss. The influence of friction loss on bearing temperature rise is linear, while the influence of electromagnetic loss on bearing temperature rise is non-linear. With the increase in vehicle speed, the overall bearing temperature will increase, and no matter how the vehicle speed changes, the main factor that causes the temperature increase of bearing components is always the electromagnetic loss of IWM. [ABSTRACT FROM AUTHOR]

This study presents an optimum vehicle architecture along with a design methodology that optimizes the motor power, battery capacity, and propulsion ratio for two different driving profiles using Genetic Algorithm (GA) and Whale Optimization Algorithm (WOA). A virtual electric vehicle model was created in MATLAB/Simulink and validated with real-world driving. The vehicle architecture was optimized with GA and WOA based on the cost, range, gradability, and maximum speed outputs obtained from the "hybrid driving" and "urban driving" behaviours. According to the results obtained in the study, it was found that GA optimization can create a vehicle architecture suitable for long-distance and high-performance driving and can provide shorter optimization times. On the other hand, it was seen that WOA optimization can create vehicle architectures with lower costs, higher maximum speeds, and improved gradability in urban driving. It was found that e-motor 4 and battery 2 can provide the most optimum vehicle architecture solution on a component basis. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of electric vehicle (EV) range anxiety and inadequate charging station, a driver assistance technology is presented here. The proposed technology assists a driver to maintain an efficient driving strategy (EDS) that minimizes energy consumption toward increasing the driving range. Additionally, it provides an anticipated range of EV corresponding to its current battery charge which alerts the driver to take a decision for required charging. EDS is determined by solving a multi-objective optimization problem corresponding to the current route characteristics. Optimization problem considers three primary objectives, minimization of energy consumption and travel time and maximize journey comfort, and follows an EV drive model. Effectiveness of the proposed technology is identified by analyzing the effect of adopting EDS in a micro-trip planning with an E-bus based on experimental drive data. A drive model for the E-bus is formulated and validated through comparative analysis of speed, motor current, and voltage that are obtained in simulation and experimentation. Such analysis was performed independently in four micro-trips identified from E-bus drive data following a typical route. It was found that the driver assistance technology reduces both energy consumption and travel time than that found by following a random driving strategy. [ABSTRACT FROM AUTHOR]

The transportation sector is undergoing an ever-increasing fast development, shifting from traditional fossil fuel-based transport to ultra-low emission electrified transport. To accelerate this transformation, appropriate planning and operation of charging stations (CSs) especially fast and ultra-fast CSs is of utmost importance. The initial and most important step toward optimum planning of CSs is to develop models for the prediction of electric vehicle (EV) load demand in order to estimate future charging profiles. Thus, a stochastic EV load model should be employed to get an accurate estimation of the total EV charging load regrading numerous interdependent uncertainties. The paper specially provides a critical review regarding different uncertainties related to EV load demand and various stochastic modeling approaches. For this purpose, related research works reported between 2005 and 2023 were gathered, screened, and summarized. Then, selected research papers are evaluated in terms of stochastic modeling of EV load demand. The stochastic approaches were categorized in two main groups of conventional and fast charging modes with regard to probability density functions, Monte Carlo Simulation, Fuzzy method, Markov chain, artificial neural networks, Copulas, and hybrid modes. Next, details of each methodology by highlighting related cons and pros were provided. It was obtained that most research works took into account three to five random variables (RVs) in-average for stochastic studies. In addition, various test and real-world networks throughout the world were employed to validate the obtained results. Finally, some potential future research areas in the field of stochastic CS planning and operation are presented. HIGHLIGHTS: A comprehensive review of the different types of EVs and CSs, A technical review of different RVs and uncertainties related to stochastic EV load demand, A complete review of various stochastic methods for EV load demand modeling, and An outlook on identifying future research perspectives in the field. [ABSTRACT FROM AUTHOR]

In cold, moisture-rich winter environments, window fogging represents a substantial safety hazard for drivers. Electric vehicles often incorporate heat pump systems to address challenges such as dehumidification and heating specific to cold weather. Therefore, it is essential to evaluate the dehumidification and heating efficiency of these systems through focused research. This study presents a dual-evaporator heat pump system designed specifically for electric vehicles, equipped with two distinct modes for dehumidification and heating. The research examines how factors such as inlet air volume and the degree of opening of the electronic expansion valve affect the system's dehumidification and heating performance. Experimental analyses were conducted to explore the system's response under various conditions of inlet air humidity and compressor speed in both modes. Results suggest that increasing inlet air volume improves dehumidification effectiveness but may reduce heating performance. Likewise, a wider opening of the electronic expansion valve enhances heating but could decrease dehumidification efficiency. Importantly, the study indicates that when the relative humidity of the inlet air exceeds 70 %, a single evaporator mode is more effective for dehumidification. However, when the relative humidity is below 70 %, the dual evaporator mode is more advantageous, showing better heating performance. [ABSTRACT FROM AUTHOR]

• A transient numerical model of scroll compressor for electric vehicle is established. • Errors are within 9.5 % between the simulation and experimental results. • The effects of discharge port size and shape on compressor performance are studied. • Power consumption reduces by 5.82 % as discharge port diameter rises from 5.2 to 9 mm. • Composite-shaped discharge port displays the best performance compared with others. Scroll compressor with short scroll profile length shows great potential in electric vehicles for its compact structure and better reliability, while shorter scroll profile results in less compression time and under-compression in most cases. To investigate the influence of discharge port structure on compressor performance, a three-dimensional transient model of scroll compressor for electric vehicle is established. The theoretical and experimental results exhibit good consistency within a deviation of 9.5 %. With this validated model, the effects of discharge port size and shape on compressor performance are studied respectively. When the circular discharge port diameter increases from 5.2 mm to 9.0 mm, the power consumption and discharge temperature can be reduced respectively by 5.82 % and 5.29 %. The discharge mass flow rate can be promoted by 1.09 %. The discharge pressure fluctuation and imbalance between the upside and downside chambers enhanced obviously. To further improve the discharge flow characteristics, different discharge port shapes were employed including waist-shaped, arc-shaped and composite-shaped port. Compared with 5.2 mm circular port, the power consumption with composite-shaped port reduces by 5.81 % and mass flow grows by 1.26 %. Results indicated changes in discharge port structure parameters have greater influence on power consumption rather than mass flow rate. [ABSTRACT FROM AUTHOR]

The gearbox of an electric vehicle operates under the high load torque and axial load of electric vehicles. In particular, the bearings that support the shaft of the gearbox are subjected to several tons of axial load, and as the mileage increases, fault occurs on bearing rolling elements frequently. Such bearing fault has a serious impact on driving comfort and vehicle safety, however, bearing faults are diagnosed by human experts nowadays, and algorithm-based electric vehicle bearing fault diagnosis has not been implemented. Therefore, in this paper, a deep learning-based bearing vibration signal processing method to diagnose bearing fault in electric vehicle gearboxes is proposed. The proposed method consists of a deep neural network learning stage and an application stage of the pre-trained neural network. In the deep neural network learning stage, supervised learning is carried out based on two acceleration sensors. In the neural network application stage, signal processing of a single accelerometer signal is performed through a pre-trained neural network. In conclusion, the pre-trained neural network makes bearing fault signals stand out and can utilize these signals to extract frequency characteristics of bearing fault. [ABSTRACT FROM AUTHOR]

Numerous studies are currently focused on improving the performance and efficiency of electric vehicles (EVs). This research aims to evaluate the necessity for a practical testing methodology to simulate real-world driving scenarios by comparing the driving range measured on a chassis dynamometer with the ranges observed under various actual driving conditions. Tests were conducted on the chassis dynamometer using the multi-cycle test (MCT) mode, employing the urban dynamometer driving schedule (UDDS) and the highway fuel economy driving test (HWFET). Subsequently, we assessed the energy efficiency of three routes compliant with the real-driving emissions-light duty vehicles (RDE-LDV) regulations under real-world driving conditions. Our findings revealed disparities in energy efficiency ranging from 10.8 to 22.9% when driving on the same route and up to 29.3% when driving on different routes. This study highlights the importance of tailoring information provision, such as certification tests, to each country's environmental context. [ABSTRACT FROM AUTHOR]

As there is a great need for high-quality electricity on the distribution side, distribution side generation (DSG) has become increasingly important. The increased weight of EVs on the distribution side is the cause of this. There are numerous power quality mitigation techniques employed to address this type of issue, but many of the solutions suggest the usage of a separate device, such as an active power filter. But while construction the DSG the solution to this problem may be addressed using the proposed solution in this paper. Power quality (PQ) problems are being caused by the grid's integration of Photo-Voltaic (PV) and its application to all connected loads. With the aid of Direct Quardrature (DQ) controller and Multicarrier Space Vector Pulse Width Modulation (SVPWM) technology, the overall power quality disturbance is decreased. A Simulink model for the PV-EV-Grid system was built to measure voltage and current Total Harmonic Distortion (THD) percentages under linear, non-linear, and Plug in Hybrid Vehicle (PHEV) load situations. The model shows that the THD values are well within the IEEE 519. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach that leverages a hybrid deep learning framework called the Squirrel Search-optimized Attention-Deep Recurrent Neural Network (SS-ADRNN) to optimize the management of charging stations, ensuring efficient resource allocation while safeguarding user data and minimizing operational costs. The SS-ADRNN model incorporates squirrel search optimization, which is inspired by the foraging behaviour of squirrels, to dynamically adjust charging station operations based on environmental conditions and demand patterns. Additionally, attention mechanisms are employed to prioritize relevant input features, enabling the model to focus on critical information during decision-making processes. Deep recurrent neural networks (RNNs) are utilized to capture temporal dependencies in charging station data, allowing for more accurate predictions and adaptive control strategies. Experimental evaluations demonstrate the effectiveness and feasibility of the proposed SS-ADRNN-based approach in real-world scenarios. The results showcase significant improvements in the detection of malicious traffic and cost minimization compared to traditional charging station management methods. Overall, this research contributes to advancing the field of intelligent charging station optimization, offering a robust and adaptable solution for EV and UAV charging infrastructures that prioritize both security and operational efficiency. [ABSTRACT FROM AUTHOR]

This article presents a model-free control (MFC) through ultra-local model and algebraic estimation for a traction control system (TCS). The model-free control uses a first-order ultra-local model, whose parameters are calculated through algebraic estimation and is valid only around the current operation of the system. The basic longitudinal model of the vehicle is presented, emphasizing the wheel slip dynamics and its relationship with the proposed ultra-local model. This relation is used to assess the parameter of the ultra-local model which multiplies the input and is defined as a function of the vehicle speed. The controller performance is demonstrated for different parameter choices and the robustness is verified for subtle changes of the tire-road adhesion coefficient, as the algebraic estimator compensates for uncertainties and deterministic disturbances and leads the system to the desired slip. The controller is compared with a gain-scheduled proportional-integral-derivative (GS-PID) control with gains scheduled in the vehicle speed. Although MFC and GS-PID exhibit similar performance, the primary advantage of the proposed controller is its simplicity and independence from physical models. [ABSTRACT FROM AUTHOR]

The global shift towards electric vehicles (EVs) underscores the critical need for reliable battery performance and safety. Lithium-ion batteries, particularly Li-NMC (lithium nickel manganese cobalt oxide), are widely adopted for their balanced functional and performance characteristics. However, the advancement of batteries with higher nickel content and reduced manganese and cobalt introduces challenges, including increased susceptibility to thermal runaway and degradation, especially under abusive conditions like over-discharge. This study addresses significant research gaps by developing a machine learning (ML) algorithm for the early detection and predictive maintenance of over-discharged Li-NMC batteries. Current methods often fail to identify and mitigate the effects of continuous cycling, which can release harmful free radicals such as singlet oxygen (1 O 2 ) and superoxide ( O 2 - ) that accelerate degradation. Our ML approach utilizes supervised learning, feature engineering, and model optimization, leveraging key input features such as voltage, time, and cycle count which are derived from extensive battery life testing. To validate our model, we conducted scanning electron microscopy energy-dispersive spectroscopy (SEM–EDS), galvanostatic charge–discharge (GCD) tests, and rate capability tests. The proposed ridge regression model achieved a mean absolute error (MAE) of 0.11422%, a mean squared error (MSE) of 0.02313%, and an R-squared (R2) value of 0.99, outperforming other models such as Decision Trees (DT), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Gradient Boosting (GB), and Lasso Regression. Our model addresses key shortcomings of existing methods, particularly in predicting degradation in precycled batteries subjected to fault induction. The insights gained contribute to a robust control strategy for EV battery management, enabling proactive maintenance, timely battery replacement, and enhanced system reliability and safety, effectively addressing the long-term challenges in battery health management. [ABSTRACT FROM AUTHOR]

The penetration of electric vehicle (EV) in automobile market is very much dependent on the battery technology. Its size, weight, and cost are issues of concern. To effectively utilize the battery expertise, precise estimate of state of charge (SoC) is vital which greatly depends on the battery model. Current models lack consideration for variations in battery capacity over their lifespan. This paper develops a battery model which depicts the depletion of battery capacity with its life. Subsequently, this model has been utilized for estimation using advanced Kalman filtering (KF) algorithms. For the developed model, the design and effectiveness of the cubature Kalman filter (CKF) is applied as a proposed robust state-estimator for this problem. Moreover, a comparative analysis was undertaken with existing non-linear KFs based on performance metrics. The optimal choice of estimator is identified, through the results obtained from the Octave/MATLAB simulation. The outcomes show CKF algorithm based SoC estimator is superior to others in ensuring high accuracy, strong robustness even under changes in initial conditions (i.e., initial SoC, process and sensor noise levels), system's ability to converge quickly while ensuring that the maximum error in state of charge (SoC) estimation remains within 1% after convergence. [ABSTRACT FROM AUTHOR]

High-performance automotive thermal management systems with environment-friendly refrigerants are essential for achieving carbon peaking and carbon neutrality goals. In this study, an R290 vapor-injection heat pump system for electric vehicles is developed and experimentally investigated. The effects of refrigerant charge mass, injection pressure, and in-cabin air temperature are analyzed in ambient temperatures from −30°C to 0°C. The results show that the vapor-injection system can increase the coefficient of performance (COP) and heating capacity by 14.3% and 15.9% at 0°C/20°C (ambient/in-cabin temperature) compared with the basic system, and this increase becomes more significant at −20°C/20°C with improvements of 32.5% and 38.1%, respectively. At a lower ambient temperature of −20°C, increasing refrigerant charge mass contributes to a more pronounced increase in heating capacity than at 0°C, which results from the more significant increase in injection mass flow. The optimal COP at various injection pressures are 2.07 and 1.63 at 0°C and −20°C ambient temperatures, corresponding to the relative injection pressures of 0.60 and 0.57, and the injection flow ratios of 0.23 and 0.29, respectively. At −30°C/0°C, a COP of 1.69 can be achieved. [ABSTRACT FROM AUTHOR]

The increasing prevalence of electric vehicles (EVs) urges charging station operators (CSOs) to expand their networks. However, currently CSOs operate independent EV charging service platforms separately, which poses inconvenience for EV users and hinders the efficient utilization of charging infrastructure. This paper introduces a consortium blockchain-enabled EV charging service platform (CB-EVCSP), which leverages novel orderly charging guidance models (OCGMs) encoded as smart contracts to integrate and coordinate the efforts of CSOs. The CB-EVCSP not only mitigates trust and security concerns but also enhances the utilization of charging facilities. Both multinomial logit (MNL) and mixed MNL models are developed to characterize the interaction between charging station recommendations and EV users' choice behaviors, where a coefficient of variation based fairness criterion is used to ensure distribution fairness. We provide equivalent reformulations and design efficient constraint generation algorithms. Numerical experiments are conducted to validate the effectiveness of the proposed models and algorithms. [ABSTRACT FROM AUTHOR]

Selecting the appropriate charger for electric vehicles (EVs) is crucial for enhancing performance, with non-isolated DC-DC converters playing a significant role in charging EV batteries. The efficient conversion of input power into output as per the requirement is main perspective in the design of DC-DC converters. This paper delves into the landscape of non-isolated DCDC converters utilized in EV charging, emphasizing their pivotal role. Additionally, it introduces a novel approach by incorporating machine learning-based pulse width modulation (PWM) control for the buck DC-DC converter. By integrating machine learning algorithms into the control scheme, the efficiency and performance of the charging system can be greatly enhanced, resulting in improved overall EV operation. This innovative application of machine learning not only optimizes charging efficiency but also enables adaptability to varying input/output conditions, ultimately leading to more efficient and effective charging processes for EVs. [ABSTRACT FROM AUTHOR]

Renewable energy systems have become integral components of the electrical grid, offering environmental benefits and cost-effective power generation. Technological advancements have introduced internet of things (IoT) devices with robust data collection and execution capabilities. Solar photovoltaic systems, reliant on unpredictable solar radiation, require hybrid systems incorporating various renewable energy sources and energy storage to ensure system stability. Successful operation necessitates data gathering through IoT devices, which have played a crucial role in enhancing system sustainability. This paper provides a comprehensive review of the role of IoT in photovoltaic systems and energy storage, highlighting its significant contributions to system efficiency, fault detection, output prediction, system stability, and load management. The study underscores the critical dependence of advancements in the renewable energy sector on IoT systems. [ABSTRACT FROM AUTHOR]

A proposed approach for efficient energy management for lithium-ion battery and supercapacitor hybrid energy storage system is outlined in this study. The primary aim is to ensure the electric vehicle receives a stable and high-quality supply of electricity. The suggested management strategy focuses on maintaining the bus voltage at a consistent level while meeting the varying load demands with high-quality power across different scenarios. To achieve this, a management controller is employed, which utilizes a metaheuristics technique to define the parameters of the integral sliding mode control. The supercapacitor units are responsible for controlling the direct current (DC) bus, while the lithium-ion battery plays a role in balancing power distribution on the common line. This study evaluates the potential benefits of integrating salp swarm algorithm techniques into the controller's performance. The findings of the study suggest combining metaheuristic optimization methods with integral sliding mode control. Can lead to improvements in power quality. The proposed management algorithm not only efficiently allocates power resources but also safeguards them, ultimately ensuring a consistent and high-quality power supply for the electric vehicle. [ABSTRACT FROM AUTHOR]

The lack of standardization in electric vehicle battery recycling units poses a significant challenge, impeding efficient battery recycling and material extraction processes. However, digital technologies such as blockchain and sensor networks offer potential solutions by enabling improved tracking and tracing of battery life cycles, thereby enhancing effective recycling. The purpose of this study is to provide current literature by identifying prime factors that strengthen the adoption of digital technology in electric vehicle battery recycling units. Through an extensive review of literature and inputs from experts, 11 drivers are identified. The combined application of Total Interpretive Structural Modelling and Cross Impact Matrix Multiplication applied to classification was utilized to determine the inter-relationships and establish a priority order among the identified drivers. The outcomes show that real-time data monitoring and coordination are the most influential drivers, while cost reduction exhibits the highest dependence among the influencing factors for adopting digital technology in electric vehicle battery recycling units. Additionally, this study undertakes an analysis of driver interactions and proposes a roadmap to adopt Digital Technologies in electric vehicle battery recycling units. The findings of this study can help practitioners and firms in the battery recycling business develop strategies for sustainably recycling electric vehicle batteries through the adoption of digital technology. [ABSTRACT FROM AUTHOR]

Fuel cell-based electric vehicles (EVs) are gaining popularity in the automotive industry due to strict carbon emissions and fuel efficiency regulations. Fuel cells have inherently low voltage characteristics, making it challenging to interface with EV drive systems. This work proposes a unique topology implementing a non-isolated high step-up DC-DC converter to integrate the Proton Exchange Membrane Fuel (PEMF) cell with the EV motor drive. The converter combines a modified Single-Ended Primary Inductor Converter (m-SEPIC) with two winding-coupled inductors, enabling high voltage gain, minimizing conduction losses, and ensuring high efficiency. The output power of a fuel cell is highly dependent on cell temperature and membrane water content, making a maximum power point tracking controller essential for extracting optimal power from the fuel cell stack. The optimum power point of the PEMF cell is identified using a Neural Network (NN)-based Maximum Power Point Tracking (MPPT) technique, employing the Radial Basis Function Network (RBFN) approach compared to the Perturb & Observe (P&O) method. The performance of the RBFN MPPT method is validated under various temperature conditions of the fuel cell system. An extensive analysis and comparison with alternative converters validate the efficacy of the proposed system. The converter is designed for an input voltage of 24 V and boosts it to an output voltage of 240 V, with high static voltage gain, high power density, and minimized total losses. The system is tested and validated through both simulation and real-time implementation using the dSPACE 1104 real-time controller. A 200 W, 3000 rpm, 240 V VSI-based BLDC motor drive integrated with the proposed converter topology is developed as a real-time setup in the laboratory which would confirm the converter’s functionality. Both theoretical analysis and real-time outcomes affirm the suitability of the proposed converter topology for electric vehicle applications. [ABSTRACT FROM AUTHOR]

Although the development of electric vehicle (EV) technology offers opportunities for reducing CO2 emissions through the electrification of transportation, the integration of EVs into distribution systems poses a significant challenge to the reliable operation of existing protection systems. As the penetration level of EVs continues to rise, the fault current characteristic of the distribution system changes, resulting in load de‐energization, equipment damage, and reduced reliability. This paper develops a protection scheme for preserving coordination between main and backup overcurrent relays considering various penetration levels and locations of integrated EVs. By modifying the EV charger control system, the proposed scheme limits the fault current contribution of adopted EV charge stations into the fault point. The developed scheme does not alter the structure of the available protection system of the distribution network and is compatible with both old and nonprogrammable relays. Furthermore, it does not require communication links. The effectiveness of the proposed scheme is validated through several case studies on the Isfahan distribution network. The findings indicate that the operating time of the backup relay in the conventional protection system exceeds the thermal limit at 100% penetration level of EVs installed upstream of this relay as it reaches 1810 ms, while by using the proposed strategy, this time reduces to 776 ms, preserving protection coordination between the main and backup relays. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, a two‐stage bidirectional DC/DC converter is suggested to handle the energy transfer for electric vehicles between high‐voltage and low‐voltage batteries. The converter enables bidirectional power transfer, a broad range of input voltage, and a high ratio of voltage. The front stage adopts the bidirectional CLLLC resonant converter, which possesses excellent soft switching characteristics because of its symmetrical structure in both forward and reverse directions. Open‐loop fixed frequency control is used in the forward direction to set the working frequency of the switching transistors near the resonance frequency, and the voltage and current double closed loop control is employed to achieve a constant voltage ratio and efficient power transmission in the reverse direction. The rear stage adopts an interleaved parallel bidirectional Buck/Boost converter, and the interleaved parallel structure helps to lessen the output current ripple. The duty cycle secondary distribution method is used on the basis of the voltage and current double closed loop control to regulate the output voltage and make the converter operate stably. The principle and characteristics of the bidirectional DC/DC converter are analyzed, and by modeling and experimentation, the theoretical analysis's viability and correctness are confirmed. [ABSTRACT FROM AUTHOR]

For nearly two centuries, electric drives have been used in transportation. Nevertheless, they were not always favored by designers. The century-long dominance of heat engines led to the disregard of numerous challenges associated with the operation of electric drive systems. One of these issues is the optimization of energy consumption by an electric vehicle. This publication proposes an electronic Energy Consumption Optimizer (ECO) that predictively uses information about the shape of the route and speed limits on its individual sections to control the motor speed and gear changes in the gearbox. This work presents the structure of the optimizer system and the developed control algorithms. Additionally, electric motor excitation control was used, which may have contributed to reducing the power and weight of the electric drive motor. Simulation studies carried out using WLTP test cycles and cycles from real road routes showed the potential to decrease energy consumption for vehicle movement by approximately 10%. [ABSTRACT FROM AUTHOR]

In the context of increasing global macroeconomic policy uncertainty, the effectiveness of Chinese government subsidies in promoting the business resilience of electric vehicle (EV) enterprises and the sustainable development of the EV industry has attracted significant attention. This paper utilizes panel data from Chinese listed EV companies from 2013 to 2022 to examine the impact of government subsidies on the business resilience of these enterprises. It also analyzes the moderating roles of corporate ESG (Environmental, Social, and Governance) performance and technological capability. This study finds that government subsidies significantly enhance business resilience, particularly for companies with a high ESG performance and a strong technological capability, which can better leverage these subsidies to further enhance their resilience amidst market fluctuations and uncertainties. Moreover, as an external support measure, government subsidies complement internal corporate factors like ESG performance and technological capability, promoting the sustainable development of the industry. Policymakers should consider corporate ESG performance and technological capability when designing subsidy programs to maximize their effectiveness. [ABSTRACT FROM AUTHOR]

The current study introduces an optimal planning and operational framework for a Distribution Network (DN) that integrates Photovoltaic (PV)-green Hydrogen (H 2)-based energy system with Electric Vehicle Charging Station (EVCS). An efficient operational strategy is proposed considering both short-term H 2 storage (STHS) and long-term H 2 storage (LTHS), ensuring uninterrupted power supply. Besides, a novel Improved Harris Hawk Optimization method is developed to enhance the global search capabilities and convergence rate in optimizing the capacity of Solar module, Electrolyzer, and Fuel cells. The objective of the proposed model is to minimize the device costs, energy loss, and carbon emissions, adhering to all operational constraints. The proposed work is tested on 33-bus radial DN and 51-bus real Indian DN considering the time frame of 10 years. The analysis reveals that the PV-STHS-LTHS configuration, reduces total planning costs by 12% and 15% as compared to the base case in the 33-bus and 51-bus systems, respectively. Additionally, the integration of both daily and seasonal storage facilities with PV systems leads to a reduction in carbon emissions by 76% and 81% in the 33-bus and 51-bus systems, respectively. Besides the carbon reduction, the incorporation of daily and cross-seasonal H 2 storage increases oxygen release into the environment by 13% for the 33-bus system and 15% for the 51-bus system. • Propose an optimal planning and operation of PV-Hydrogen integrated power system. • Incorporate daily and seasonal H 2 storage facility for uninterrupted power supply. • Proposed planning is tested on real Indian 51-bus practical distribution network. • Modelled uncertain EV load and solar irradiance using probability density functions. • Development of novel IHHO with balanced exploration and exploitation for optimization. [ABSTRACT FROM AUTHOR]