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This work describes the Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI) architecture for the co-simulation of distribution and transportation systems to evaluate EV charging impacts on electric distribution systems of a large metropolitan area and the surrounding rural regions with high fidelity. The current co-simulation is applied to Oakland and Alameda, California, and in future work will be extended to the full San Francisco Bay Area. It uses the HELICS co-simulation framework to enable parallel instances of vetted grid and transportation software programs to interact at every model timestep, allowing high-fidelity simulations at a large scale. This enables not only the impacts of electrified transportation systems across a larger interconnected collection of distribution feeders to be evaluated, but also the feedbacks between the two systems, such as through control systems, to be captured and compared. The findings are that with moderate passenger EV adoption rates, inverter controls combined with some distribution system hardware upgrades can maintain grid voltages within ANSI C.84 range A limits of 0.95 to 1.05 p.u. without smart charging. However, EV charging control may be required for higher levels of charging or to reduce grid upgrades, and this will be explored in future work.

Electromobility contributes to decreasing environmental pollution and fossil fuel dependence, as well as increasing the integration of renewable energy resources. The increasing interest in using electric vehicles (EVs), enhanced by machine learning (ML) algorithms for intelligent automation, has reduced the reliance on. This shift has created an interdependence between power, automatically, and transportation networks, adding complexity to their management and scheduling. Moreover, due to complex charging infrastructures, such as variations in power supply, efficiency, driver behaviors, charging demand, and electricity price, advanced techniques should be applied to predict a wide range of variables in EV performance. As the adoption of EVs continues to accelerate, the integration of ML and especially deep learning (DL) algorithms will play a pivotal role in shaping the future of sustainable transportation. This paper provides a mini review of the ML impacts on mobility electrification. The applications of ML are evaluated in various aspects of e-mobility, including battery management, range prediction, charging infrastructure optimization, autonomous driving, energy management, predictive maintenance, traffic management, vehicle-to-grid (V2G), and fleet management. The main advantages and challenges of models in the years 2013–2024 have been represented for all mentioned applications. Also, all new trends for future work and the strengths and weaknesses of ML models in various aspects of mobility transportation are covered. By discussing and reviewing research papers in this field, it is revealed that leveraging ML models can accelerate the transition to electric mobility, leading to cleaner, safer, and more sustainable transportation systems. This paper states that the dependence on big data for training, the high uncertainty of parameters affecting the performance of electric vehicles, and cybersecurity are the main challenges of ML in the e-mobility sector.

Electric mobility is attracting significant attention in the current era due to its environmental benefits, sustainable transportation options, and the absence of carbon emissions. However, challenges such as the high price of batteries, inefficient charging techniques, and compatibility linking the charging station with electric vehicles (EVs) must be addressed. This article reviews advancements and identifies challenges in charging infrastructure for electric mobility. This study incorporates and analyzes an integrated review of approximately 223 research articles. Current research trends and states of charging infrastructure are prepared as per the Web of Science (WoS) database from 2013 to 2023. In light of recent extensions in wireless power transfer technology, including capacitive, inductive, and magnetic gear topology, are presented to advance the charging infrastructure. Different charging tactics based on power source, such as level-1 AC, level-2 AC, level-3 DC fast, and level-3 DC ultra-rapid charging, related to charging infrastructure are addressed. The vehicle-to-grid (V2G) integration methodology is addressed to construct a smart city by presenting the transfer of power and related data through linkage and moving systems. The exploration of artificial intelligence, global connectivity of electric vehicles (EVs), sun-to-vehicle (S2V), and vehicle-to-everything (V2X) techniques with EVs is conducted to enhance and progress the charging infrastructure. Key barriers associated with charging infrastructure are identified.

ISO 15118 is an international standard for charging communication between electric vehicles and charging infrastructure. Among the series of ISO 15118, ISO 15118-4, ISO 15118-5, ISO 15118-9, and ISO 15118-21 (under development) define active conformance testing methodologies where the test system engages with the system under test as a primary actor and tests whether the counterparty is complying with the standard. However, such a full-fledged test system is not appropriate during system development and field testing. In this paper, a passive conformance testing approach that monitors the packets between the electric vehicle and the charger and performs conformance testing against the requirements of ISO 15118 is proposed. This novel approach exhibits practicality yet comprehensive testing capability for field testing because of its passive access to the packets with deep analysis for the conformance. TestShark, a modified packet analysis tool, showed that the passive conformance testing system can help developers and operators analyze problems in charging communication and assess the quality of implementation with respect to ISO 15118.

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.

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%.

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.

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.

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.

This paper examines the transition of lithium-ion batteries from electric vehicles (EVs) to energy storage systems (ESSs), with a focus on diagnosing their state of health (SOH) to ensure efficient and safe repurposing. It compares direct methods, model-based diagnostics, and data-driven techniques, evaluating their strengths and limitations for both EV and ESS applications. This study underscores the necessity of accurate SOH diagnostics to maximize battery reuse, promoting sustainability and circular economy objectives. By providing a comprehensive overview of the battery lifecycle—from manufacturing to recycling—this research offers strategies for effective lifecycle management and cost-effective, environmentally sustainable secondary battery applications. Key findings highlight the potential of second-life EV batteries in ESSs. The integration of the considered diagnostic methods was shown to extend battery lifespan by up to 30%, reduce waste, and optimize resource efficiency, which is crucial for achieving circular economy objectives. This paper’s insights are crucial for advancing sustainable energy systems and informing future research on improving diagnostic methods for evolving battery technologies.

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. LSD-MADDPG modifies the conventional MADDPG framework by limiting data sharing during centralized training to only discretized strategic information. During execution, it relies solely on local information, eliminating post-training data exchange. This approach addresses critical challenges commonly faced by EMS solutions serving dynamic, increasing-scale communities, such as communication delays, single-point failures, scalability, and nonstationary environments. By leveraging and sharing only strategic information among agents, LSD-MADDPG optimizes decision-making while enhancing training efficiency and safeguarding data privacy—a critical concern in the community EMS. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening the community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings by ensuring fair alignment between individual buildings’ energy management actions and community-wide goals, highlighting its potential for advancing future smart community energy management.

This paper describes an approach in improving the performance of the position sensorless control of electric oil pumps with a permanent magnet synchronous motor. Electric oil pumps are widely applied for the lubricating and cooling of e-Axles in HEV and BEV which operate from −40 to 130 °C. The accuracy of the estimation obtained from the sensorless control based on the motor model depends on the accuracy of motor parameters and input values. At a lower speed and lower temperature region, the parameter variation and input measurement errors have gained greater influence over the accuracy of the estimation. This paper describes how to overcome this weakness of the sensorless drive via applying a robust position estimator with electrical parameter adaptation and compensation of a phase voltage measurement error. Experimental results with various types of pumps show the effectiveness of the proposed method.

The rapid development of electric vehicles (EVs) has brought great challenges to the power grid, so improving the EV load prediction accuracy is crucial to the safe operation of the power grid. Aiming at the problem of insufficient consideration of spatial dimension information in the current EV charging load forecasting research, this study proposes a forecasting method that considers spatio-temporal node importance information. The improved PageRank algorithm is used to carry out the importance degree calculation of the load nodes based on the historical load information and the geographic location information of the charging station nodes, and the spatio-temporal features are initially extracted. In addition, the attention mechanism and convolutional network techniques are also utilized to further mine the spatio-temporal feature information to improve the prediction accuracy. The results on a charging station load dataset within a city in the Hebei South Network show that the model in this study can effectively handle the task of forecasting large fluctuations and long time series of charging loads and improve the forecasting accuracy.

The fast charging of electric vehicles (EVs) has stood out prominently as an alternative for long-distance travel. These charging events typically occur at public fast charging stations (FCSs) within brief timeframes, which requires a substantial demand for power and energy in a short period. To adequately prepare the system for the widespread adoption of EVs, it is imperative to comprehend and establish standards for user behavior. This study employs agglomerative clustering, kernel density estimation, beta distribution, and data mining techniques to model and identify patterns in these charging events. They utilize telemetry data from charging events on highways, which are public and cost-free. Critical parameters such as stage of charge (SoC), energy, power, time, and location are examined to understand user dynamics during charging events. The findings of this research provide a clear insight into user behavior by separating charging events into five groups, which significantly clarifies user behavior and allows for mathematical modeling. Also, the results show that the FCSs have varying patterns according to the location. They serve as a basis for future research, including topics for further investigations, such as integrating charging events with renewable energy sources, establishing load management policies, and generating accurate load forecasting models.

The transportation industry is experiencing a switch towards electrification. Availability of electric vehicle (EV) charging infrastructure is very critical for broader acceptance of EVs. The increasing use of OBCs, due to their cost-effectiveness and ease of installation, necessitates addressing key challenges. These include achieving high efficiency and power density to overcome space limitations and reduce charging times. Additionally, the growing interest in bidirectional power flow, allowing EVs to supply power back to the grid, highlights the importance of innovative OBC solutions. This review article provides a thorough analysis of the current advancements, challenges, and prospects in EV on-board charger technology. It aims to offer a comprehensive review of OBC architectures, components, technologies, and emerging trends, guiding future research and development. Addressing these challenges is essential to enhance the efficiency, reliability, and integration of OBCs within the broader EV ecosystem.

Electric vehicles are an emerging topic in organizations and society. Access to economically and environmentally workable sustainable technologies has become a priority in the face of catastrophic climate change and the discussion surrounding it. This study examines the academic literature to shed light on factors, trends, emerging models, and critical perspectives on electric vehicles and their relationship to value creation, strategic management, and sustainability. This research uses a bibliometric analysis method to explore the dynamic interaction between these essential elements of the literature. The source is the Scopus database. The analysis uses VOSviewer software 1.6.20, focusing on the bibliographic coupling of documents, which was presented as a network visualization map. It emphasizes search terms proving the emerging need for innovation strategies in automotive industry organizations, consumers, and public entities to create value within a global sustainability logic. This study highlights the relationship between the electric vehicle industry and the value creation strategy and sustainability in a digital world.

This research aims at proposing an alternative to improve the efficiency of electric vehicles (EVs) and reduce greenhouse gas (GHG) emissions in the context of electric mobility. A photovoltaic and wind hybrid energy system was installed in a Chok S2 electric vehicle. In addition, a charge equalization system was included to balance and maximize the performance of each of the EV’s five batteries connected in series. The results show a 20% improvement in vehicle efficiency after conducting tests on a 17 km Andean route. The photovoltaic system generated 535 W, while the wind system generated 135 W/s at a speed of 45 km/h. These findings highlight the potential of hybrid renewable energy systems to improve the efficiency and range of electric vehicles.

The integration of renewable energy sources (RESs) is rapidly increasing within energy systems worldwide. However, this shift introduces intermittency and uncertainty on the supply side. To hedge against RES intermittency, demand-side flexibility introduces a practical solution. Therefore, further studies are required to unleash demand-side flexibility in power systems. This flexibility is relevant across various sectors of power systems, including residential, industrial, commercial, and agricultural sectors. This paper reviews the key aspects of demand-side flexibility within the residential sector. To achieve this objective, a general introduction to demand flexibility across the four sectors is provided. As a contribution of this paper, and in comparison with previous studies, household appliances are classified based on their flexibility and controllability. The flexibility potential of key residential demands, including heat pumps, district heating, electric vehicles, and battery systems, is then reviewed. Another contribution of this paper is the exploration of demand-side flexibility scheduling under uncertainty, examining three approaches: stochastic programming, robust optimization, and information-gap decision theory. Additionally, the integration of demand flexibility into short-term electricity markets with high-RES penetration is discussed. Finally, the key objective functions and simulation software used in the study of demand-side flexibility are reviewed.

The present paper proposes the use of fractional derivatives in the definition of sliding function, giving a new mode control applied to induction motor drives in electric vehicle (EV) applications. The proposed Fractional-Order Sliding Mode Direct Torque Control-Space Vector Modulation (FOSM-DTC-SVM) strategy aims to address the limitations of conventional control techniques and mitigate torque and flux ripples in induction motor systems. The paper first introduces the motivation for using fractional-order control methods to handle the nonlinear and fractional characteristics inherent in induction motor systems. The core describes the proposed FOSM-DTC-SVM control strategy, which leverages a fractional sliding function and the associated Lyapunov stability analysis. The efficiency of the proposed strategy is validated via three scenarios. (i) The first scenario, where the acceleration of the desired speed is defined by pulses, leading to Dirac impulses in its second derivative, demonstrates the advantage of the proposed control approach in tracking the desired speed while minimizing flux ripples and generating pulses in the rotor pulsation. (ii) The second scenario demonstrates the effectiveness of filtering the desired speed to eliminate Dirac impulses, resulting in smoother rotor pulsation variations and a slightly slower speed response while maintaining similar flux ripples and stator current characteristics. (iii) The third scenario consists of eliminating the fractional derivatives of the pulses existing in the expression of the control, leading to the elimination of Dirac impulses. These results demonstrate the potential of the FOSM-DTC-SVM to revolutionize the performance and efficiency of EVs. By incorporating fractional control in the control scheme for PV-powered EVs, the paper showcases a promising avenue for sustainable transportation.

Accurate forecasting of electric vehicle (EV) power consumption per unit mileage serves as the cornerstone for determining diurnal variations in EV charging loads. To enhance the prediction accuracy of EV power consumption per unit mileage, this paper proposes a modelling method grounded in an improved Ant Colony Optimization-Support Vector Regression (ACO-SVR) framework. This method integrates the effects of both temperature and speed on the power consumption per unit mileage of EVs. Initially, we analyze the influence mechanism of driving speed and ambient temperature on EV power consumption, elucidating the relationship between power consumption per unit mileage and these factors. Subsequently, we construct an ACO-SVR model utilizing an improved ant colony optimization algorithm, fitting the relationship between power consumption, speed, and temperature to derive the EV power consumption per unit mileage model. Finally, leveraging operational data from EVs in Guangdong, Hong Kong, and Macao as a case study, we validate the energy consumption model of EVs by considering factors such as ambient temperature and driving speed. The results demonstrate that the model proposed in this paper is both accurate and effective.

This paper introduces a novel convolutional neural network (CNN) architecture tailored for state of charge (SoC) estimation in battery management systems (BMS), accompanied by an advanced optimization technique to enhance training efficiency. The proposed CNN architecture comprises multiple one-dimensional convolutional (Conv1D) layers followed by batch normalization and one-dimensional max-pooling (MaxPooling1D) layers, culminating in dense layers for regression-based SoC prediction. To improve training effectiveness, we introduce an advanced dynamic k-decay learning rate scheduling method. This technique dynamically adjusts the learning rate during training, responding to changes in validation loss to fine-tune the training process. Experimental validation was conducted on various drive cycles, including the dynamic stress test (DST), Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), United States 2006 Supplemental Federal Test Procedure (US06), and Worldwide Harmonized Light Vehicles Test Cycle (WLTC), spanning four temperature conditions (−5 °C, 5 °C, 25 °C, 45 °C). Notably, the test error of DST and US06 drive cycles, the CNN with optimization achieved a mean absolute error (MAE) of 0.0091 and 0.0080, respectively at 25 °C, and a root mean square error (RMSE) of 0.013 and 0.0095, respectively. In contrast, the baseline CNN without optimization yielded higher MAE and RMSE values of 0.011 and 0.014, respectively, on the same drive cycles. Additionally, training time with the optimization technique was significantly reduced, with a recorded time of 324.14 s compared to 648.59 s for the CNN without optimization at room temperature. These results demonstrate the effectiveness of the proposed CNN architecture combined with advanced dynamic learning rate scheduling in accurately predicting SoC across various battery types and drive cycles. The optimization technique not only improves prediction accuracy but also substantially reduces training time, highlighting its potential for enhancing battery management systems in electric vehicle applications.

The location of Electric Vehicle Charging Stations (EVCSs) is gaining significant importance as part of the conversion to a full-electric vehicle fleet. Positive or negative impacts can be generated mainly based on the quality of service offered to customers and operational efficiency, also potentially involving the electrical grid to which the EVCSs are connected. The EVCS location problem requires an in-depth and comprehensive analysis of geographical, market, urban planning, and operational aspects that can lead to several potential alternatives to be evaluated with respect to a defined number of features. This paper discusses the possible use of a multi-criteria decision-making approach, considering the differences between multi-objective decision making (MODM) and multi-attribute decision-making (MADM), to address the EVCS location problem. The conceptual evaluation leads to the conclusion that the MADM approach is more suitable than MODM for the specific problem. The identification of suitable attributes and related features is then carried out based on a systematic literature review. For each attribute, the relative importance of the features is obtained by considering the occurrence and the dedicated weights. The results provide the identification of the most used attributes and the categorization of the selected features to shape the proposed MADM framework for the location of the electric vehicle charging infrastructure.

Electric mobility is a sustainable alternative for mitigating carbon emissions by replacing the conventional fleet. However, the low availability of data from charging stations makes planning energy systems for the integration of electric vehicles (EVs) difficult. Given this, this work focuses on developing an adaptive computational tool for charging simulation, considering many EVs and mobility patterns. Technical specifications data from many EVs are considered for charging simulation, such as battery capacity, driving range, charging time, charging standard for each EV, and mobility patterns. Different simulations of charging many EVs and analyses of weekly charging load profiles are carried out, portraying the characteristics of the different load profiles and the challenges that system planners expect. The research results denote the importance of considering different manufacturers and models of EVs in the composition of the aggregate charging load profile and mobility patterns of the region. The developed model can be adapted to any system, expanded with new EVs, and scaled to many EVs, supporting different research areas.

Energy consumption in electric vehicles is a key element of their operation, determining energy efficiency and one of its main indicators, i.e., range. Therefore, in this article, mathematical models were developed to evaluate the impact of selected factors on energy consumption in electric vehicles. The phenomenon of energy recuperation was also examined. The study used data from mileage measurements of the electric vehicle (EV) driving on a motorway and in built-up areas. The results obtained showed a strong correlation between acceleration, vehicle speed, battery power, and energy consumption. In urban conditions, engine RPM and vehicle speed had an additional impact on energy consumption. Findings from this study can be used to optimize vehicle acceleration control modules to increase their range, develop eco-driving styles for EV drivers, and better understand the energy efficiency factors of EVs.

In this work, we consider a problem from the field of power-aware scheduling in which a fleet of electric vehicles have to be charged in a minimum time. Each vehicle is equipped with a lithium-ion battery of a given capacity. The initial power used for charging each battery is known, whereas it is assumed that the power drops to zero at the moment when the battery gets fully loaded. The power usage function is linear and decreasing. The charging jobs are nonpreemptable and independent, whereas the total available amount of power is limited. The objective is to minimize the schedule length. In this paper, we analyze the case of a problem with identical jobs that already cover a wide variety of practical situations. By employing inverses of natural numbers, similar to harmonic series, we prove two properties of this case, and we also discuss the phenomenon of the stabilization of the difference between the start times of two successive jobs in a schedule. We also take under examination a few special cases of the problem. Some conclusions and directions for future research are given.

The increasing demand for wireless power transfer (WPT) systems for electric vehicles (EVs) has necessitated advancements in charging solutions, with a particular focus on speed and efficiency. However, power transfer efficiency is the major concern in static and dynamic wireless charging (DWC) design. Design consideration and improvements in all functional units are necessary for an increase in overall efficiency of the system. Recently, different research works have been presented regarding DWC at the power converter, coil structure and compensators. This paper provides a comprehensive review of power converters incorporating high-order compensation topologies, demonstrating their benefits in enhancing the DWC of EVs. The review also delves into the coupling coil structure and magnetic material architecture, pivotal in enhancing power transfer efficiency and capability. Moreover, the high-order compensation topologies used to effectively mitigate low-frequency ripple, improve voltage regulation, and facilitate a more compact and portable design are discussed. Furthermore, optimal coupling and different techniques to achieve maximum power transfer efficiency are discussed to boost magnetic interactions, thereby reducing power loss. Finally, this paper highlights the essential role of these components in developing efficient and reliable DWC systems for EVs, emphasizing their contribution to achieving high-power transfer efficiency and stability.

This study combines the Nash–Cournot competition model and the stochastic optimization model to examine the impact of electric vehicle (EV) quantity fluctuations on microgrid operations, aiming to optimize energy usage in a competitive electricity market. Integrating distributed energy resources and bidirectional charging, microgrids offer a novel approach for energy optimization, aiding in renewable energy generation, peak demand management, and emission reduction. Empirical evidence highlights benefits in Taiwan’s electricity market and net-zero emissions target by 2050, with a case study demonstrating enhanced local renewable energy generation due to EVs and microgrid integration. As the number of EVs increases, electricity sales from microgrids decline, but electricity purchases remain stable. The degree of electricity liberalization also influences the supply and demand dynamics of the electricity market. Microgrids selling electricity only to the main grid increases total power consumption by 65.55 million MWh, reducing the market share of the state-owned utility (Taipower). Conversely, allowing retailers to purchase from microgrids increases total consumption by 30.87 million MWh with a slight market share decrease for Taipower. This study contributes to providing an adaptable and flexible general model for future studies to modify and expand based on different scenarios and variables to shape energy and environmental policies.

This paper proposes a novel physics-based equivalent circuit model of the lithium-ion battery for electric vehicle applications that has comprehensive electrochemical significance and an acceptable level of complexity. Initially, the physics-based extended single particle (ESP) model is improved by adding a correction term to mitigate its voltage bias. Then, the equivalent circuit model based on the improved extended single particle (ECMIESP) model is derived. In this model, the surface state of charge (SOC) of solid particles is approximated using a capacity and multi first-order resistance-capacity equivalent circuits with only two lumped parameters. The overpotential of electrolyte diffusion is approximated using a first-order resistance-capacitance equivalent circuit. The electrochemical reaction overpotential is characterized by a nonlinear resistance. The voltage accuracies of ECMIESP and conventional 2RC equivalent circuit model (ECM2RC) are compared across the entire SOC range under various load profiles. The results demonstrate that the ECMIESP model outperforms ECM2RC model, particularly at low SOC or when the electrochemical reaction overpotential exceeds 50 mV. For instance, the ECMIESP model shows an 820.4 mV reduction in voltage error compared to the ECM2RC model at the endpoint during a 2C constant current discharge test. Lastly, the ECMIESP model was used for SOC estimation with extended Kalman filter, resulting in significantly improved accuracy compared to the conventional ECM2RC model. Therefore, the ECMIESP model has great potential for real-time applications in enhancing voltage and SOC estimation precision.

Electric vehicles (EVs) have seen significant growth due to the increasing awareness about environmental concerns and the negative impacts of internal combustion engine vehicles (ICEVs). The electric vehicle landscape is rapidly evolving, with EV policies, battery, and charging infrastructure and electric vehicle-to-everything (V2X) at its forefront. This review study used a bibliometric analysis of the Scopus database to investigate the development of EV technology. This bibliometric study specifically focuses on analyzing electric vehicle trends, policy implications, lithium-ion batteries, EV battery management systems, charging infrastructure, EV smart charging technologies, and V2X. Through this detailed bibliometric analysis discussion, we aim to provide a better understanding of holistic EV technology and inspire further research in electric vehicles. The analysis covers the period from 1990 to 2022. This bibliometric analysis underscores the interplay of electric vehicle policies, technology, and infrastructure, specifically focusing on developments in battery management and the possibility of V2X technology. In addition, this bibliometric analysis suggests the synchronization of international electric vehicle policy, advancement of battery technology, and promotion of the use of EV smart charging and V2X systems. This bibliometric analysis emphasizes that the expansion of EVs and sustainable mobility relies on a comprehensive strategy that encompasses policy, technology, and infrastructure. This bibliometric analysis recommends fostering collaboration between different sectors to drive innovation and advancements in electric vehicle technology.

As a core component of the powertrain system of Electric Logistics Vehicles (ELVs), the gearbox is crucial for ensuring the reliability and stability of ELV operations. Traditional fault diagnosis methods for gearboxes primarily rely on the analysis of vibration signals during operation. This paper presents research on diagnosing gear tooth wear faults in ELV powertrains using motor current signals. Firstly, an experimental test platform was constructed based on the structural principle of the powertrain of ELV models. Subsequently, a pure electric light truck powertrain gearbox with tooth wear was tested. Time–frequency domain analysis, amplitude analysis, ANOVA analysis, kurtosis analysis, and zero−crossing points analysis were used to analyze the U−phase current of the motor connected to the gearbox to study the characteristics of the phase current of the drive motor after tooth wear. The results indicate that while the time–frequency domain characteristics of the U−phase currents are not significantly altered by tooth wear faults, the amplitude, variance, and kurtosis of the current increase with the severity of the wear. Conversely, the number of zero−crossing points decreases. These findings provide valuable insights into new methodologies for diagnosing faults in ELV powertrain systems, potentially enhancing the efficiency and effectiveness of troubleshooting processes.

Li-ion batteries determine the lifespan of an electric vehicle. High power and energy density and extensive service time are crucial parameters in EV batteries. In terms of safe and effective usage, a precise cell model and SoC estimation algorithm are indispensable. To provide an accurate SoC estimation, a current- and temperature-dependent SoC estimation algorithm is proposed in this paper. The proposed SoC estimation algorithm and equivalent circuit model (ECM) of the cells include current and temperature effects to reflect real battery behavior and provide an accurate SoC estimation. For including current and temperature effects in the cell model, lookup tables have been used for each parameter of the model. Based on the proposed ECM, the unscented Kalman filter (UKF) approach is utilized for estimating SoC since this approach is satisfactory for nonlinear systems such as lithium-ion batteries. The experimental results reveal that the proposed approach provides superior accuracy when compared to conventional methods and it is promising in terms of meeting electric vehicle requirements.

Among many emerging technologies, battery electric vehicles (BEVs) have emerged as a prominent and highly supported solution to stringent emissions regulations. However, despite their increasing popularity, key challenges that might jeopardize their further spread are the lack of charging infrastructure, battery life degradation, and the discrepancy between the actual and promised all-electric driving range. The primary focus of this paper is to formulate an integrated energy and thermal comfort management (IETM) strategy. This strategy optimally manages the electrical energy required by the heating, ventilation, and air conditioning (HVAC) unit, the most impacting auxiliary in terms of battery load, to minimize battery life degradation over any specific drive cycle while ensuring the actual cabin temperature hovers within the permissible tolerance limit from the reference cabin temperature and the driver-requested traction power is always satisfied. This work incorporates a state-of-health (SOH) estimation model, a high-fidelity cabin thermodynamics model, and an HVAC model into the forward-approach simulation model of a commercially available BEV to showcase the impact and efficacy of the proposed IETM strategy for enhancing battery longevity. The instantaneous optimization problem of IETM is solved by the golden-section search method leveraging the convexity of the objective function. Simulated results under different driving scenarios show that the improvement brought by the proposed ITEM controller can minimize battery health degradation by up to 4.5% and energy consumption by up to 2.8% while maintaining the cabin temperature deviation within permissible limits from the reference temperature.

The charging behavior of electric vehicle users is highly stochastic, which makes the short-term prediction of charging load at electric vehicle charging stations difficult. In this paper, a data-driven hybrid model optimized by the improved dung beetle optimization algorithm (IDBO) is proposed to address the problem of the low accuracy of short-term prediction. Firstly, the charging station data are preprocessed to obtain clear and organized load data, and the input feature matrix is constructed using factors such as temperature, date type, and holidays. Secondly, the optimal CNN-BiLSTM model is constructed using convolutional neural network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM), which realizes the feature extraction of the input matrix and better captures the hidden patterns and regularities in it. Then, methods such as Bernoulli mapping are used to improve the DBO algorithm and its hyperparameters; for example, hidden neurons of the hybrid model are tuned to further improve the model prediction accuracy. Finally, a simulation experiment platform is established based on MATLAB R2023a to validate the example calculations on the historical data of EV charging stations in the public dataset of ANN-DATA, and comparative analyses are carried out. The results show that compared with the traditional models such as CNN, BiLSTM and PSO-CNN-BiLSTM, the coefficient of determination of the model exceeds 0.8921 and the root mean square error is maintained at about 4.413 on both the training and test sets, which proves its effectiveness and stability.

This paper proposes a new approach to the design of smart charging systems. It aims to separate the role of the Smart Charging Service Provider (SCSP) from the role of the Charge Point Operator (CPO) to provide real flexibility and efficiency of mass deployment. As interoperability is required for this purpose, the challenge is to use standard equipment and protocols in the design of the smart charging Energy Management System (EMS). The use of an Open Charge Point Interface (OCPI) is crucial for an interface between the EMS and the Charge Point Operator. The smart charging EMS developed has been implemented and successfully tested with two CPOs, with different use cases: (1) EV charging infrastructure at office buildings, and (2) EV charging infrastructure installed at a public car park facility.

Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically heat pumps (HPs) and electric vehicles (EVs). Additionally, it addresses photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to obtain economic benefits for private households and, at the same time, facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with phase change material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of an active distribution grid with eight houses using Power Factory software and DIgSILENT simulation language (DSL). This scalable size of the active distribution network facilitates the careful study of symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours.

This article is devoted to the ecological comparison of electric and internal combustion engine vehicles throughout their entire life cycle, from mining to recycling. A scientifically based approach to a comprehensive environmental assessment of the impact of vehicles on the environment has been developed. To analyze the impact on the environmental situation, aspects such as the consumption of natural resources, waste generation, electricity consumption, emission of harmful substances into the atmosphere, water consumption, and greenhouse gas emissions are taken into consideration. As a result of comparing the environmental impacts of vehicles, it was found that natural resources consumption and production of industrial waste from electric vehicles (EV) is 6 times higher than from internal combustion engine vehicles (ICEV). Harmful substance emissions and greenhouse gas emissions from EV production are 1.65 and 1.5 times higher, respectively. The EV total electricity consumption is 1.4 times higher than that of ICEVs. At the same time, it was revealed that during operation, EVs have higher energy consumption and emit more harmful substances into the atmosphere, but EVs produce less greenhouse gas emissions. It means that at different life cycle stages, EVs have a much higher negative impact on the environment compared to gasoline engine vehicles.

With the shortage of fossil fuels and the increasingly serious problem of environmental pollution, low-carbon industrial production technology has become an effective way to reduce industrial carbon emissions. Electrified steel plants based on electronic arc furnaces (EAF) can reduce most carbon emissions compared with traditional steel production methods, but the production steps have fixed electricity consumption behavior, and impact loads are easily generated in the production process, which has an impact on the stability of the power system. EV has the characteristics of a mobile energy storage unit. When a large number of EVs are connected to the power grid, they can be regarded as distributed energy storage units with scheduling flexibility. Through the orderly scheduling of EVs, the spatial–temporal transfer of EV charging and discharging load can be realized. Therefore, the EV situated in the steel plant’s distribution network node has the capacity to be utilized by providing peak shaving and valley filling services for the steel production load. This study proposes an operation optimization scheduling method for EVs and steel plants. Taking the lowest overall operating cost as the objective, an optimal scheduling model considering EVs operation, steel plant, and distributed generator is established. Based on the IEEE-33 node distribution network model considering distributed generators, the proposed model is simulated and analyzed, and the effectiveness of the EV steel plant operation optimization scheduling strategy is investigated.

Aiming at the problem of source-load uncertainty caused by the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs) into modern power system, a robust optimal operation scheduling algorithm for regional integrated energy systems (RIESs) with such uncertain situations is urgently needed. Based on this background, aiming at the problem of the irregular charging demand of EV, this paper first proposes an EV charging demand model based on the trip chain theory. Secondly, a multi-RIES optimization operation model including a shared energy storage station (SESS) and integrated demand response (IDR) is established. Aiming at the uncertainty problem of renewable energy, this paper transforms this kind of problem into a dynamic robust optimization with time-varying parameters and proposes an improved robust optimization over time (ROOT) algorithm based on the scenario method and establishes an optimal scheduling mode with the minimum daily operation cost of a multi-regional integrated energy system. Finally, the proposed uncertainty analysis method is verified by an example of multi-RIES. The simulation results show that in the case of the improved ROOT proposed in this paper to solve the robust solution of renewable energy, compared with the traditional charging load demand that regards the EVs as a whole, the EV charging load demand based on the trip chain can reduce the cost of EV charging by 3.5% and the operating cost of the multi-RIES by 11.7%. With the increasing number of EVs, the choice of the starting point of the future EV trip chain is more variable, and the choice of charging methods is more abundant. Therefore, modeling the charging demand of EVs under more complex trip chains is the work that needs to be studied in the future.

As the adoption of distributed energy resources (DERs) grows, the future of electricity distribution systems is confronted with significant challenges. These challenges arise from the transformation of consumers into prosumers and the resulting increased system complexity, leading to more pressure on the distribution grids. To address this complexity, a Digital Twin framework is designed to simulate DERs within distribution grids effectively. This framework is structured around four key modules: DERs, the electricity distribution grid, the energy management system, and the consumers. It incorporates a communication interface to facilitate interactions among these modules and includes considerations for grid topologies and demand-side configurations. The framework allows for the exploration of various DER adoption rates and capacities. The validation of this framework involves case studies on two Danish distribution grids with scenarios incorporating rooftop photovoltaic (PV) systems, batteries, and electric vehicles, considering different combinations of these technologies. The findings demonstrate the framework’s ability to depict the states of the grid, PV systems, electric vehicles, and battery systems with a 10 min resolution over periods ranging from a day to over a decade.

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model’s precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model’s predictive capabilities. Comparing the model’s predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach.

With the increasing number of electric vehicles (EVs), their uncoordinated charging poses a great challenge to the safe operation of the power grid. In addition, traditional individual-EV scheduling models may be difficult to solve due to the increasing number of constraints. Therefore, this paper proposes a cluster-based EV scheduling model. Firstly, electric vehicle clusters (EVCs) are formed based on the charging and discharging preferences of EV users and the expected time for EVs to leave. Secondly, the EVC energy and power boundary aggregation method based on the Minkowski addition algorithm is proposed. Finally, for the sake of reducing user charging cost and distribution network energy loss, and smoothing the daily load curve, an EVC scheduling model for EV participation in grid auxiliary services is proposed. The optimization model includes the reactive-power compensation of EV charging piles. The simulation results show that the proposed EVC scheduling model can greatly reduce the solution time compared to traditional individual-EV scheduling model. The model has high potential to be applied to large-scale EV scheduling. The reactive-power compensation provided by EV charging piles improves the voltage quality of the grid and enables more EVs to be connected to the grid.

This study investigates the impact of renewable energy penetration on system stability and validates the performance of the (Proportional-Integral-Derivative) PID-(reinforcement learning) RL control technique. Three scenarios were examined: no photovoltaic (PV), 25% PV, and 50% PV, to evaluate the impact of PV penetration on system stability. The results demonstrate that while the absence of renewable energy yields a more stable frequency response, a higher PV penetration (50%) enhances stability in tie-line active power flow between interconnected systems. This shows that an increased PV penetration improves frequency balance and active power flow stability. Additionally, the study evaluates three control scenarios: no control input, PID-(Particle Swarm Optimization) PSO, and PID-RL, to validate the performance of the PID-RL control technique. The findings show that the EV system with PID-RL outperforms the other scenarios in terms of frequency response, tie-line active power response, and frequency difference response. The PID-RL controller significantly enhances the damping of the dominant oscillation mode and restores the stability within the first 4 s—after the disturbance in first second. This leads to an improved stability compared to the EV system with PID-PSO (within 21 s) and without any control input (oscillating more than 30 s). Overall, this research provides the improvement in terms of frequency response, tie-line active power response, and frequency difference response with high renewable energy penetration levels and the research validates the effectiveness of the PID-RL control technique in stabilizing the EV system. These findings can contribute to the development of strategies for integrating renewable energy sources and optimizing control systems, ensuring a more stable and sustainable power grid.

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method.

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

The electrification of the transport sector has emerged as a game changer in addressing the issues of climate change caused by global warming. However, the unregulated expansion and simplistic approach to electric vehicle (EV) charging pose substantial risks to grid stability and efficiency. Intelligent charging techniques using Information and Communication Technology, known as smart charging, enable the transformation of the EV fleets from passive consumers to active participants within the grid ecosystem. This concept facilitates the EV fleet’s contribution to various grid services, enhancing grid functionality and resilience. This paper investigates the optimal infrastructure design for a smart charging system within the Monash microgrid (Clayton campus). We introduce a centralized Grid-to-Vehicle (G2V) algorithm and formulate three optimization problems utilizing linear and least-squares programming methods. These problems address tariff structures between the main grid and microgrid, aiming to maximize aggregator profits or minimize load fluctuations while meeting EV users’ charging needs. Additionally, our framework incorporates network-aware coordination via the Newton–Raphson method, leveraging EVs’ charging flexibility to mitigate congestion and node voltage issues. We evaluate the G2V algorithm’s performance under increasing EV user demand through simulation and analyze the net present value (NPV) over 15 years. The results highlight the effectiveness of our proposed framework in optimizing grid operation management. Moreover, our case study offers valuable insights into an efficient investment strategy for deploying the G2V system on campus.

Air pollutants and greenhouse gases (GHGs) represent major challenges in our era, contributing to climate change and global health issues. These problems arise from a variety of well-known sources, including motor vehicles. Almost all nations, Thailand included, have formulated and implemented policies to curb greenhouse gas (GHG) emissions in line with the requirements and commitments of the Paris Agreement. The evaluation of specific air pollutants and GHG emissions originating from road vehicles utilises the Thailand database, referencing the year 2019. Data intersections from 2019 to 2022 are grounded in actual data collected from relevant departments in Thailand, while projections for 2023–2030 are forecasted based on the baseline year. The secondary database used in the International Vehicle Emission model is adjusted according to real-world driving data to accurately reflect country-specific emission factors. Dynamic emission factors for specific air pollutants and GHGs are evaluated and integrated with the average Vehicle Kilometres Travelled (VKT) for each vehicle category. The Business-As-Usual (BAU) scenario is then examined, based on existing policies aimed at reducing air pollutants and GHG emissions in Thailand’s transport sector. These policies include strategies for the adoption of electric vehicles and the promotion of public transport to reduce VKT. Under the BAU scenario, the overall number of road vehicles in Thailand, including passenger cars, motorcycles, pickups, vans, trucks, and buses, is expected to increase by approximately 6.58% by 2030, leading to a rise in specific air pollutants and GHG emissions compared to the 2019 baseline. However, by adhering to Thailand’s strategies and transitioning to new electric passenger cars and buses, greenhouse gas emissions and specific air pollutants from the road transport sector will be significantly reduced.

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

An optimal daily scheme is presented to coordinate a large population of heterogeneous battery electric vehicles when charging in daytime work solar-powered parking lots and discharging into homes during evening peak-demand hours. First, we develop a grid-to-vehicle strategy to share the solar energy available in a parking lot between vehicles where the statistics of their arrival states of charge are dictated by an aggregator. Then, we develop a vehicle-to-grid strategy so that vehicle owners with a satisfactory level of energy in their batteries could help to decongest the grid when they return by providing backup power to their homes at an aggregate level per vehicle based on a duration proposed by an aggregator. Both strategies, with concepts from Mean Field Games, would be implemented to reduce the standard deviation in the states of charge of batteries at the end of charging/discharging vehicles while maintaining some fairness and decentralization criteria. Realistic numerical results, based on deterministic data while considering the physical constraints of vehicle batteries, show, first, in the case of charging in a parking lot, a strong to slight decrease in the standard deviation in the states of charge at the end, respectively, for the sunniest day, an average day, and the cloudiest day; then, in the case of discharging into the grid, over three days, we observe at the end the same strong decrease in the standard deviation in the states of charge.

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

Electric vehicles (EVs) are propelled by electric traction drive systems (ETDSs), which consist of various components including an electric motor, power electronic converter, and gear box. During manufacturing, end-of-line testing is the ultimate step for ensuring the quality and performance of electric motors in electric vehicle (EV) traction drive systems (ETDSs). The outcome of end-of-line testing of electric motors is significantly influenced by the tolerances of their structural parameters, such as stator inner and outer diameters, magnet dimensions, air gaps, and other geometric parameters. The existing literature provides insights into parametric sensitivity, offering guidance for enhancing the reliability of end-of-line testing. In this manuscript, the importance of end-of-line testing and the role of manufacturing tolerances of e-motor structural parameters in the manufacturing process of ETDSs are discussed. The impact of tolerances of e-motor structural parameters on the test results, such as torque, efficiency, back EMF, and e-NV (noise and vibration), is investigated. Finally, key challenges and research gaps in this area are identified, and recommendations for future research to mitigate the drawbacks of end-of-line testing are provided.