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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]

The number of electric vehicle (EV) in residential areas is growing rapidly, resulting in large‐scale charging of EVs connected to the distribution network. This poses a challenge to the safe and stable operation of the distribution network. In order to cope with this challenge, it is crucial to achieve accurate EV charging load prediction. However, current researches on EV charging load prediction suffer from insufficient data feature mining and lower prediction accuracy. To address this issue, this paper proposes a SE‐GRU‐MA residential EV charging load prediction method that incorporates multi‐source data feature mining. The proposed method employs a multi‐source data feature mining approach based on Pearson correlation analysis, which enhances the training efficiency and prediction accuracy of the prediction model. Additionally, this study develops a prediction model based on SE‐GRU‐MA hybrid network to achieve accurate EV charging load prediction. Simulation cases on actual history data validate that the proposed feature mining method can effectively promote prediction accuracy, and proposed SE‐GRU‐MA prediction model exhibits superior prediction capability in comparison to existing models. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

High-speed reducers in electric vehicles, characterized by high rotation speeds, heavy loads, large helix angles, and high contact ratios, are prone to tooth surface scuffing due to high sliding speeds. This scuffing is caused by adhesion wear from excessive instantaneous friction flash temperatures. The prevailing approach to gear scuffing analysis relies on the standard formula method, which is a relatively rudimentary technique. This method lacks the precision required to accurately assess the intricate distribution of tooth surface flash temperature (TSFT), limiting its efficacy in targeted tooth optimization. This study introduces an enhanced semi-analytical method to calculate TSFT and analyzes its variation under different conditions: increased tooth number and reduced module, altered pressure angle, and varied helix angle. The aim is to understand how these geometric parameters affect TSFT and the scuffing load capacity of high-speed reducer gears. This study calculates load distribution and TSFT under peak operating conditions and shows that increasing the tooth number, pressure angle, and helix angle can reduce maximum TSFT by more than 30%, improving scuffing safety and load capacity. However, these improvements must consider the gear's allowable bending safety factor and bearing service life. The research concludes that optimizing these geometric parameters can significantly enhance the scuffing load capacity of gearsets. [ABSTRACT FROM AUTHOR]

In this article, a 12 switch 31 L multi-level inverter (MLI) is proposed with the benefits of least switching devices for electric vehicle applications. In most electric vehicles (EV), conventional inverters are utilized so the lifetime of electric vehicle induction motors is reduced due to the high THD level and high voltage stress. To rectify this, a new inverter topology is proposed with minimum switching devices by increasing the level to 31, and also the THD should be maintained within IEEE standards. This inverter topology is constructed with variable DC sources (PV system) along with required capacitors and a polarity changer. The specific feature of this topology is that it can generate any level of voltage with minimum switching devices, less voltage stress, minimum THD, and less cost are tabulated in the comparison. This type of inverter can also applicable for high voltage (HV) applications and grid-connected systems. Furthermore, the proposed topology is simulated with MATLAB software and the downscale prototype model is developed using the DSPIC30f2010 controller. [ABSTRACT FROM AUTHOR]

Modernizing the Electric Vehicle (EV) charging infrastructure is essential for the widespread adoption of electric mobility. This research addresses the imperative need for enhancing the power performance of photovoltaic (PV) grid-connected inverters for EV fast charging applications. The system integrates Voltage-Oriented Control (VOC) for the PV inverter, a High-Gain Interleaved (Single Ended Primary Inductance Converter) SEPIC-Cuk (HGISC) converter, and a Bald Eagle Search Optimized Adaptive Neuro Fuzzy Inference System (BESO-ANFIS)) algorithm. VOC with Proportional Resonant (PR) ensures optimized energy transfer to grid, while the HGIBC converter enhances power performance. The proposed BESO-ANFIS Maximum Power Point Tracking (MPPT) dynamically tracks the PV array’s Maximum Power Point (MPP) under varying conditions. Additionally, a bidirectional battery converter in the energy storage system optimizes power usage. The synergistic implementation of these advanced controller results in a comprehensive solution, showcasing improved power output, grid integration, and efficient solar energy utilization for EV fast charging. MATLAB simulations and experiments demonstrate 97% efficiency and reduced Total Harmonic Distortion (THD) of 0.98%, positioning this research as an advanced solution for EV charging infrastructure enhancement. [ABSTRACT FROM AUTHOR]

Transport electrification is a crucial element of the ongoing energy transition, essential for achieving carbon peaking and carbon neutrality goals. The proliferation of electric vehicles (EVs) introduces significant challenges to power distribution network stability due to their aggregated charging load in residential areas, particularly during peak electricity consumption periods. This paper proposes a method to predict the spatiotemporal distribution of EV charging demand in residential areas using geographic information points of interest (POI) data features and a decision‐making model. Utilizing real historical data, probability distribution models for EV users' arrival times and charging characteristics were constructed using Gaussian Mixture Models (GMM). The spatiotemporal characteristics of EV travel and charging behaviors were analyzed, and a comprehensive charging decision model incorporating both emergency and stochastic scenarios was developed. The model's efficacy in capturing the probability distributions of characteristic variables was validated through a case study. The results demonstrate the model's potential for accurately predicting EV charging demand, providing valuable insights for infrastructure planning and resource allocation. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

To address the urban distribution route optimization issue in cold chain logistics, the new model of multi-temperature co-distribution with soft time windows for electric vehicles was proposed for the low-cost and high-efficiency needs of logistics enterprises. Based on the cooler and insulated boxes, the different temperature-level goods were delivered simultaneously by ordinary electric vehicles for improving vehicle utilization. The improved ant colony algorithm was proposed to solve the problem, and the 2-optimization (2-opt) algorithm was combined with the ant colony algorithm to enhance the local search capability. The effectiveness of the model and algorithm was verified through case analysis based on the Solomon dataset. The results show that compared to the single temperature distribution, the proposed multi-temperature co-distribution can reduce delivery costs and improve efficiency. As the width of time windows is expanded, the number of vehicles is decreased, and the delivery cost shows decreasing trend. After the number of vehicles is reduced to the minimum, the total cost continues to decline slowly due to the continuous reduction of incentive costs and spoilage costs. [ABSTRACT FROM AUTHOR]

The fast expansion of the electric vehicle market has led to a significant increase in the demand for traction batteries, an essential element in these vehicles that provide the opportunity to achieve low-carbon and environmentally friendly growth and carbon neutrality. By analyzing the network structure and key trading countries from 2000 to 2021, this research uses multilayer network theory to explore the trade patterns and the evolution of the global cathode material industrial chain of traction batteries. Our findings indicate the following: (1) The industrial networks display multi-core trading country characteristics. Trade connections among the top 20 countries, which account for more than 80% of the global trade volume, have strengthened. (2) Over time, the geographic center of trade has shifted from being primarily focused in Europe, North America, and East Asia to embracing the entire world, including regions such as Africa, South America, and Oceania. (3) In 2021, Australia overtook Japan as the main exporter, which held the top position in 2000. Similarly, China surpassed the United States, which was the top importer in 2000. (4) Changes in global trade relationships have affected the trading habits of the top-ranked countries. [ABSTRACT FROM AUTHOR]

As the penetration of electric vehicles (EVs) increases, an understanding of EV operation characteristics becomes crucial in various aspects, e.g., grid stability and battery degradation. This can be achieved through analyzing large amounts of EV operation data; however, the variability in EV data according to the user complicates unified data analysis and identification of representative patterns. In this research, a framework that captures EV charging characteristics in terms of charge–discharge area is proposed using actual field data. In order to illustrate EV operation characteristics in a unified format, an individual EV operation profile is modeled by the probability distribution of the charging start and end states of charge (SoCs).Then, hierarchical clustering analysis is employed to derive representative charging profiles. Using large amounts of real-world, vehicle-specific EV data in South Korea, the analysis results reveal that EV charging characteristics in terms of the battery charge–discharge area can be summarized into seven representative profiles. [ABSTRACT FROM AUTHOR]

The widespread diffusion of electric vehicles poses new challenges in the field of fault diagnostics. Past studies have been focused mainly on machines designed for industrial applications, where the operating conditions and requirements are significantly different. This work presents a review of the most recent studies about fault diagnosis techniques in electrical machines feasible for traction applications, with a focus on the most adopted approaches of the last years and on the latest trends. Considerations about their applicability for electric vehicle purposes, along with some areas that require further research, are also provided. [ABSTRACT FROM AUTHOR]

The aim of this paper is to examine the relationship between the operating parameters of electric vehicles (EVs) and parameters related to road conditions. The data for analysis came from urban driving trips conducted at different times of day. The average energy consumption was the dependent variable in the model. The following parameters were used as independent variables: the stopping time;; the journey time; the average speed; the quartiles of speed, acceleration, and deceleration, and their maximum values; the number of braking; stops; and the acceleration time and regenerative braking. A multiple linear regression model was developed to predict the average energy demand of an EV based on its kinematic parameters. The proposed model enables the analysis of the influence of various factors related to the route and driving style of the driver on the average energy consumption in the vehicle. [ABSTRACT FROM AUTHOR]

This paper evaluates the reliability of smart home energy management systems (SHEMSs) in a residential community with an unreliable power grid and power shortages. Unlike the previous works, which mainly focused on cost analysis, this research assesses the reliability of SHEMSs for different backup power sources, including photovoltaic systems (PVs), battery storage systems (BSSs), electric vehicles (EVs), and diesel generators (DGs). The impact of these changes on the daily cost and the balance of energy source contribution in providing electrical energy to household loads, particularly during power outage hours, is also evaluated. To address the uncertainty of electricity market prices, a risk management approach based on conditional value at risk is applied. Additionally, the study highlights the impact of community size on energy costs and reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved using GAMS. The effectiveness of the proposed risk-based optimization approach is demonstrated through comprehensive cost and reliability analysis. The results reveal that when electric vehicles are used as backup power sources, the energy index of reliability (EIR) is not affected by market price variations and shows significant improvement, reaching approximately 99.9% across all scenarios. [ABSTRACT FROM AUTHOR]

Battery electric vehicles represent a technological pathway for reducing carbon emissions in personal road transport. However, for the widespread adoption of this type of vehicle, the user experience should be similar to that of combustion engine vehicles. To achieve this objective, a robust and reliable public charging infrastructure is essential. In Spain, the electric recharging infrastructure is growing quickly in metropolitan areas but much more slowly on roads and highways. The upcoming charging stations must be located along high-volume traffic corridors and in proximity to the Trans-European Transport Network. The main contribution of this research is to offer a method for examining the essential electricity infrastructure investments required in scenarios involving substantial electric vehicle adoption. The methodology includes a sensitivity analysis of fleet composition and market share, recharging user behavior, charging station density, and vehicle efficiency improvements. To this end, the authors have developed a simplified probabilistic model, addressing the effect of the involved parameters through a comprehensive scenario analysis. The results show that the actual number of high-capacity charging plugs on Spanish roads is significantly lower than the European regulation requirements for the year 2030 considering an electric vehicle market share according to the Spanish Integrated National Energy and Climate Plan 2021–2030 objectives and it is far from the necessary infrastructure to cover the expected demand according to the traffic flow. Under these circumstances, the charging peak power demand reaches over 7.4% of the current Spanish total power demand for an electric vehicle fleet, which corresponds to only 12% of the total. [ABSTRACT FROM AUTHOR]

Platooning is one of the promising technologies that maximizes the power efficiency of electric vehicles by decreasing the distances between the vehicles. Along with the development of autonomous driving technology, platooning is expected to be commercialized. Recent studies on the operation of platooning focused on power-efficient maintenance of platooning. However, power-efficient operation strategy is also needed for practical applications. Therefore, this study deals with platooning operations that can maximize the power efficiency of electric vehicles in various operational situations. In order to derive the operation method, a mathematical model structured with an objective function that minimizes power consumption is developed. To derive the solution of the mathematical model, a hybrid genetic algorithm is applied. The numerical experiments on four different operational situations are performed to verify the validity of the model and the solution procedure. The four situations consider overall situation that can happen during the platooning stage. The stages are formation, disassembly, join and breakaway of vehicles of platoon. Those four situations are decided upon since they can represent the general situation that can happen during platooning. As a result, the power-efficient driving patterns of electric vehicles are identified. After the development of electric and systematic technology, operational technology for platooning will collaborate for the further improvement. Therefore, throughout consideration of the formation of platooning, technology will expand the sustainability of technological development. [ABSTRACT FROM AUTHOR]

The global electric vehicle (EV) market overgrew in the previous decade. This paper investigates the factors affecting EV purchase intention in the West Bank, Palestine. This study adopts the exploratory sequential mixed methods approach by conducting unstructured interviews and questionnaires in a developing country context. We obtained 384 survey responses from EV owners and non-EV owners – this study used the partial least squares structural equation modeling (PLS-SEM) tool for empirical analysis. The study results show that environmental concerns, subjective norms, cognitive status, incentive policies, and product perception significantly affect consumers' intentions to purchase EVs in the West Bank. Environmental concerns indirectly correlate with consumers' intentions to purchase EVs through attitudes as a mediator. However, perceived behavior control has no significant impact on purchasing intent. These results will help policymakers in improving transportation policies. [ABSTRACT FROM AUTHOR]

To improve the fault-tolerant ability of switched reluctance motor (SRM) drive systems and ensure the braking performance of the electric vehicle driven by in-wheel SRMs, a novel fault-tolerant control method that includes the open circuit and the short circuit is presented in this study. First, a novel modular fault-tolerant power converter, which includes traditional driving topology and modular fault-tolerant construction, is built in accordance with the traditional half-bridge power converter. This novel converter provides an advanced fault-tolerant solution for the faults of SRM drive systems. Then, combined with the corresponding detection signals, a new fault detection method is presented for accurately detecting the faults of the system. Furthermore, a new fault-tolerant control method is proposed to improve the fault-tolerant ability of the system. Notably, the fault-tolerant process is completed by employing the circuit of the traditional power converter to replace the fault circuit of the modular construct when faults are detected. Moreover, simulation results are obtained and compared. The results indicate that the proposed method can ensure vehicle braking performance, including braking comfort, braking safety, endurance mileage, and braking stability. Finally, hardware-in-the-loop is conducted, and the results demonstrate the real-time performance and validity of the proposed method. [ABSTRACT FROM AUTHOR]

Investigation into the energy consumption in electric vehicles (EVs) plays a pivotal role in determining their autonomy and assessing the electric system performance across diverse operational scenarios. This study focuses on the concept of energy regeneration, encompassing the recovery and storage of kinetic mechanical energy during braking or descent in EVs. Employing control systems in power electronics becomes necessary to establish a seamless workflow across operational quadrants to ensure efficient energy regeneration in an electric machine functioning as both a motor and a generator. To seamlessly integrate new technologies into practical applications, it is essential to conduct thorough evaluations in laboratories prior to deployment. This paper introduces an experimental platform specifically designed to analyze energy consumption and storage in EVs by emulating their powertrains in a controlled laboratory environment. The platform comprises key components for emulating the powertrain of a single-motor electric vehicle with single-axle traction, including a power converter configured in two quadrants, an energy storage system, a primary rotating electric machine, and a mechanically coupled point load torque (another motor). This paper provides a detailed guide on implementing such a laboratory and for facilitating the testing of diverse motor technologies and controllers under varied operational conditions. This comprehensive approach allows for the assessment of electromechanical system efficiency, focusing on both energy recovery and comprehensive control of electric power converters. Validation tests conducted under urban conditions and on steep terrains demonstrate the effectiveness of the platform in analyzing the energy efficiency of both the induction machine and the power controller. [ABSTRACT FROM AUTHOR]

Electric vehicles (EV) are moderately defeating more roads and replacing pollutants of classical vehicles. Due to rising EV, it is imperative to provide charging stations. However, unscheduled EVs are left because of the unavailability of adequate energy or charging slots. This paper develops an optimisation aware technique for charge scheduling in EV. The first step is the simulation of EV in the Vehicular Ad-hoc Network (VANET) model. Here, the discovery of the changing request from EVs and accessible charging stations is performed. Then, the charge scheduling algorithm is called for scheduling the EV, wherein the charge scheduling algorithm is newly modelled using Jaya-based Multi-Verse Optimizer (JMVO). The proposed JMVO is devised by combining Multi-Verse Optimiser (MVO) and Jaya optimisation algorithm. Here, a multiobjective fitness function is newly devised with certain parameters, including charging cost, user preference, remaining power and distance parameter. As per the scheduling method, the EVs are allocated to the charging station. Then, the EV charging scheduling model attributes are updated to expose the method's efficiency. The proposed JMVO outperformed with the smallest charging cost of 62.721, smallest fitness of 0.010, highest power of 2.605J and highest user convenience of 0.769. durgamahato379@gmail.com [ABSTRACT FROM AUTHOR]

Summary: This paper outlines the development of an interleaved bidirectional DC–DC converter featuring single‐phase high‐frequency isolation and employing two distinct pulse width modulations (PWMs), one for each power flow direction. The analysis of converter stages, expressions for DC voltage gains, and voltage/current stresses in continuous conduction mode are presented. The proposed converter offers several advantages, including a new modulation approach, a current ripple frequency, and voltage in two ports that are four times higher than the switching frequency. Additionally, it entails a reduction in the volume and weight of passive elements and ensures balanced current distribution between the arms. Moreover, the single‐phase transformer offers galvanic isolation between the DC bus and the battery or supercapacitor. An experimental prototype has been implemented in the laboratory to validate the mathematical and theoretical analysis for both energy flow directions under the test conditions, with a converter‐rated power of 2.5 kW, a low voltage side of 180 V, and a high voltage side of 380 V. [ABSTRACT FROM AUTHOR]

As the automotive industry undergoes a significant transition toward electric vehicles (EV), the matter of electric current discharge in motor bearings has emerged as a focal point of concern. This issue has gathered increased attention due to its potential to inflict damage upon bearing surfaces. The extent of damage inflicted by the discharges is intricately tied to the discharge energy, a factor directly influenced by both bearing capacitance and discharge voltage. In pursuit of a deeper understanding of discharge voltage dynamics within EV motor bearings, electric discharge tests were conducted using in-house modified single ball-on-disk contact and full bearing test equipment. Our test results at the single ball-on-disk contact show that discharge voltage adheres to a probabilistic distribution. Moreover, parallel investigations at the bearing level have yielded corroborative findings, reinforcing conclusions drawn from the single contact tests. These collective efforts contribute to a comprehensive understanding of electric discharge phenomena in motor bearings, essential for developing effective mitigation strategies and advancing the reliability of EV propulsion systems. [ABSTRACT FROM AUTHOR]

To meet the temperature goal set by the Paris Agreement, cumulative CO2 emissions must be kept below 300 GtCO2. Road transportation accounted for approximately 18% of the CO2 emissions from fuel combustion in 2017. Electric vehicles (EVs) have been rapidly adopted by environmentally conscious consumers in many countries to reduce CO2 emissions. EVs have lower emission intensities than do internal combustion engine vehicles (ICEVs) in most parts of the world, except where the penetration of renewable energy is low in the energy production mix. In such places, the CO2 emissions of EVs are larger than those of ICEVs. Despite the obvious need to increase renewable energy sources to reduce CO2 emissions, Japan and many other countries around the world have yet to shift away from fossil fuels. This is due in part to carbon lock-in, which refers to prior decisions related to technologies and infrastructure that constrain the implementation of better paths toward low-carbon technologies. Coal-fired power plants are the most problematic in terms of carbon lock-in because of their high carbon intensities and long physical lives. In addition, because carbon lock-in by coal-fired power plants has a significant impact on the embodied CO2 intensity of grid power, it impacts society through products that use electric power. In this study, we propose a hybrid simulation model of lifecycle simulation and replacement simulation, considering carbon lock-in by coal-fired power plants. In the replacement simulation, we simulated the replacement of end-of-life coal- and oil-fired power plants with renewable energy power plants using a probability called the lock-in rate and estimated the changes in the embodied CO2 intensity of grid power in Japan. In the lifecycle simulation, we evaluated cumulative CO2 emissions from entire product lifecycles of ICEVs and EVs based on three different EV diffusion scenarios. The results showed that the lock-in rate of coal-fired power plants strongly affects the decarbonization effect due to the market diffusion of EVs. [ABSTRACT FROM AUTHOR]