Search

The article considers the creation of a model range of unified electric power drives for commercial electric vehicles. The peculiarity of the work is the realization of the concept of reducing the cost of production of electromechanical power units (EMPU) of electric vehicles by unification of units and components included in its composition. When forming a number of unified electric power drives, the components were subjected to a set of design, layout, technological and economic requirements ensuring the production on their basis of cargo or passenger electric vehicles with gross weight from 3.5 to 12 t, which corresponds to vehicle categories N1, N2, M2, M3. The developed main components of the unified EMPU include: asynchronous three-phase traction electric motors with power range from 80 to 300 kW, torque range from 250 to 1,000 N·m respectively, output shaft rotation frequency of 9,000 rpm and liquid cooling of stator and rotor; inverters designed to control traction motors with input voltage range from 300 to 900 V, output phase current range from 200 to 600 A and liquid-cooled power keys; reduction gearboxes with gear ratio from 3 to 4.5 and step gearboxes with the number of gears from 2 to 4. The result of works on unification of electric power drives for commercial transport also is a number of standard designs of parts and units and assembly units; normative and technical support of control (tests, analysis, measurements) of certification and quality assessment of products; establishment of requirements for technological processes, including for reduction of material, energy and labor intensity to ensure the use of low-waste technologies. When creating the electric truck MAZ-4381EE, corresponding to the vehicle category N2, the EMPU was used as a part of electric motor TAEM 130 with capacity of 130 kW and two-stage shaft gearbox. The created prototype of electric truck is characterized by almost complete localization, providing import independence of most components and intellectual component of the car.

The global surge in the electric vehicle market shows a growing commitment to sustainable transportation. However, Malaysia’s progress in EV development has been slow. This article presents the challenges to EV adoption in Malaysia. The study identifies critical issues such as inadequate charging infrastructure, uneven distribution of charging stations, high costs of ownership, and low consumer awareness, acceptance, and safety. These challenges collectively impact the adoption rate of EVs in the Malaysian market. The study also suggests potential solutions, such as expanding the charging infrastructure, developing cost-effective strategies, implementing fast charging with renewable integration, enforcing safety protocols and standards, and establishing a battery recycling industry. By addressing these key areas, the research highlights a feasible path to boost EV adoption in Malaysia that aligns with international trends and promotes a greener future.

The present paper is concerned with shedding light on the financial viability of setting up public charging infrastructure in a range of implementation situations in an Indian city and the possible role of policies and regulations to make charging business attractive. The nascency of the electric mobility sector and its fast-evolving technological landscape along with possibility of different charging use-cases make it difficult to templatize the financial evaluation of charging business. With the help of a publicly available peer-reviewed financial tool which has been specifically developed to analyze the possible financial outcome of an investment in public charging infrastructure for electric vehicles (EVs) in Indian context, the study examines the business attractiveness of setting up a public charging station in four unique use-cases such as charging at public parking, destination charging, on-the-go charging, and shared workplace charging. The study employs Project Internal Rate of Return as the primary financial yardstick that is being benchmarked with a hurdle rate or target rate of return to make the evaluation as objective as possible. The financial modelling aptly captures the dynamics of India’s EV charging market by considering the possible variations in charging infrastructure configuration, electricity tariffs and regulations, provision for land rental, charging fees and other such factors. It further demystifies the potential impact of key variables on a project’s business case. This research would help design policies, regulations, support mechanisms and terms of reference for public charging infrastructure projects in the country. Policymakers, implementing authorities, regulators, potential investors, and financial institutions are expected to benefit from the analyses.

Concerning the increasing application of plug-in electric vehicles (PEVs), planning PEV fast-charging stations (PEVF-CS) has become an important research topic. Regarding the reactive power compensation capability, the optimal planning of PEVF-CS reduces voltage deviation and power loss in the distribution network. Also, one of the basic requirements for expanding electric transportation is the optimal placement of accessible PEVF-CSs, considering the geographic information data. Therefore, the optimal placement of PEVF-CS requires attention to different geographical criteria and power distribution network constraints. In this sense, this paper aims to propose an approach that integrates the Geographic Information System (GIS) technique, Multi-Criteria Decision-Making (MCDM) method, and Mixed-Integer Nonlinear Programming to find the optimal location of a PEVF-CS in Kabul city. The first stage is decision analysis based on the GIS technique and the MCDM approach. The second stage is suitability analysis of the power distribution network constraints to improve power quality. This paper considers ten different suitability criteria, and the Technique for Order Preference Similarity to Ideal Solution (TOPSIS) is applied to rank the different candidate locations. The analysis identified Junction 4 as the optimal choice and demonstrated a significant 3.6% reduction in power loss during peak hours, decreasing from 1071 kW to 1032 kW. These results demonstrate the effectiveness of our approach in optimizing PEVF-CS placement to enhance power quality and reduce the power loss.

The investigation of machine learning models in Europe and India for predicting future sales of electric cars (EVs) worldwide has been the focus of present research. In this study, Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), Linear Regression, hybrid long- and short-term memory neural network model and gradient boosted regression trees model (LSTM-GBRT) models based machine learning techniques are compared for performance analysis to predict EV sales. Moreover, in this study, a hybrid LSTM-GBRT technique was developed, which forecasts EV sales, particularly for Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) over the next five years. During the prediction of EV sales, it has been validated that hybrid LSTM-GBRT technique has better predictive power. In addition, the present study attempts to provide estimated sales statistics for the designated locations. The results indicate that EV industry is evolving, with India showing a strong growth rate in BEV sales and establishing itself as a major participant in the global EV scenario by showing a notable increase in its share of worldwide EV sales volume by 2027. Conversely, the performance of EV sales in Europe has been analysed and validated through the proposed LSTM-GBRT model.

The key to a Battery Management System (BMS) is the accurate and real-time prediction of the State of Charge (SOC) of the power battery. Currently, there is relatively little research on the construction methods of battery models within a wide temperature range. A second-order RC equivalent circuit is selected to establish an Improved Equivalent Circuit Model (IECM) based on temperature compensation. The identification of IECM parameters is completed by using the multi strategy improvement of Artificial Bee Colony (MSIABC) algorithm combining with pulse discharge experimental data under different temperature conditions (-20 °C to 60 °C). Based on the experimental data of the UDDS condition and the hybrid dynamic condition under low temperature, high temperature, and time-varying temperature environments, the battery SOC is estimated by combining the IECM and Adaptive Extended Kalman Filtering (AEKF) algorithm. The experimental results show that compared with the conventional ECM-AEKF estimation method, IECM-AEKF has higher SOC estimation accuracy and environmental temperature adaptability.

ABSTRACTThe goal of this study is to create a dynamic model that can accurately forecast the estimated time of arrival of a bus at a specific bus stop using data obtained from the global positioning system (GPS) in Addis Ababa, Ethiopia. Both operators and customers use accurate and timely bus arrival information. In turn, it helps determine the correct placement of charging stations when green-energy vehicles are introduced in the urban mass transit system. A plethora of machine learning models have been used for prediction. Additionally, we tailored an ensemble method based on the average prediction. The performances of these machine learning methods were estimated and compared using conventional measures, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) values. The results showed that the performance of the Random Forest technique used in this study was impressive, with MAE of 0.137, MSE of 0.054, RMSE of 0.233, and R2 value of 0. 999. The findings demonstrate that the proposed method is reliable for predicting the arrival times of urban and rural buses. Next, in order to position the charging points for Electric Vehicles (EV) along specific bus routes, self-organising map (SOM) is employed to optimise EV charging point placement locations, and the result signify better performance than the kNN and DBSCAN clustering approaches.

Abstract 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 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.

The demand for high-gain, efficient, and cost-effective power converters with simple control mechanisms to connect electric vehicle batteries to the grid is increasing. This study introduces a switched capacitor-based power boost converter circuit to meet these needs. The minimal number of power switches in the circuit simplifies control operations and improves practical applicability. The proposed converter boosts the battery pack voltage by a factor of 5 and 11 for duty ratios of 0.5 and 0.8, respectively, which is significant compared to conventional boost converters. However, designing an electromagnetic interference (EMI) filter is crucial when a power converter board requires the application of a wide range of switching frequencies to enhance electromagnetic compatibility (EMC) immunity. Therefore, as the next step, the EMI/EMC filter design stages were discussed for the proposed converter. For this purpose, 15 printed circuit board (PCB) design rules were checked using the Altium Designer EMI Design Rule Checker, and board EMI/EMC compatibility was analysed. The resonance between the power and ground layers in the PCB was assessed using the plane resonance analyser. The results of simulation and laboratory tests are presented, which confirms the theoretical studies. On the basis of the software results, the points on the electronic board most susceptible to visual interferences have been identified. To minimise these EMI/EMC errors, it is suggested to add electronic components, such as capacitors, at these points according to mathematical and software findings.

The transport sector emits 18% of global CO2. Industry and consumers must adopt green mobility to reduce emissions and climate change. This will help achieve sustainability by improving efficiency and reducing greenhouse gas emissions. Thus, smart electric vehicles (SEVs) have emerged. Digital twins concept and technology may help launch SEVs to the market by analysing and optimising supporting infrastructure. This work aims to fill in the gaps between different pieces of research by giving a full review from a technical and scientifically neutral point of view. The study looks at how digital twin technology can be used in smart car systems by looking at its promise and the hurdles faced. Based on a comprehensive literature survey, this is the first in-depth look at how digital twin technology can be used in smart electric cars. The review has been organised into specific areas of the smart vehicle system, such as drive train system battery management system, driver assistance system, vehicle health monitoring system, vehicle power electronics. This review goes into detail about each component of the car to provide an overall view of the smart vehicle system as a whole. This review makes it easier to understand how digital twin technology can be utilized into each area from a scientific point of view. Lastly, the work looks at the technological and economic impact of digital twin technology, which will make considerable changes in car manufacturing processes, as well as help address current obstacles in utilizing advanced technologies.

Abstract Electric vehicles are pivotal for global climate solutions, particularly in emerging markets like Africa. Despite the continent’s clean energy potential, electric vehicle adoption faces unique challenges due to inefficiencies and reliability issues of distribution power grids. Here, we analyze the impacts of expanding electric vehicle fleets—private, commercial, and paratransit—on Nairobi’s power grid. We simulate traffic patterns, charging behaviors, and transformer utilization using local mobility data. Our results show that while electric commercial and paratransit fleets may improve power system efficiency, widespread private EV adoption could significantly strain the grid, increasing peak loads and transformer aging. Smart charging strategies could mitigate these issues, reducing potential transformer replacement costs by up to 40%. Our study highlights the importance of tailored demand management and infrastructure planning to support EV growth in African cities, providing critical insights for policymakers, utilities, and transport planners to facilitate sustainable electric mobility transitions.

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.

To adress the problems of insufficient consideration of charging pile resource limitations, discrete-time scheduling methods that do not meet the actual demand and insufficient descriptions of peak-shaving response capability in current electric vehicle (EV) optimization scheduling, edge intelligence-oriented electric vehicle optimization scheduling and charging station peak-shaving response capability assessment methods are proposed on the basis of the consideration of electric vehicle and charging pile matching. First, an edge-intelligence-oriented electric vehicle regulation frame for charging stations is proposed. Second, continuous time variables are used to represent the available charging periods, establish the charging station controllable EV load model and the future available charging pile mathematical model, and establish the EV and charging pile matching matrix and constraints. Then, with the goal of maximizing the user charging demand and reducing the charging cost, the charging station EV optimal scheduling model is established, and the EV peak response capacity assessment model is further established by considering the EV load shifting constraints under different peak response capacities. Finally, a typical scenario of a real charging station is taken as an example for the analysis of optimal EV scheduling and peak shaving response capacity, and the proposed method is compared with the traditional method to verify the effectiveness and practicality of the proposed method.

Electric vehicles have become one of the most important innovations in the automotive industry in recent years. This is not only related to technological developments, but also to its significant impact on the environment and lifestyle of global society. Lot of people do not know about the benefit of using electric vehicles for our environment. The transition from conventional vehicles to electric vehicles can really make our environment healthier and also reducing the pollution. At the same time, debates and feelings about electric vehicles continue to grow around the world. This study aims to understand the dynamics of people's feelings and opinions about electric vehicles through sentiment analysis using the FastText and IndoBERT methods. FastText is an efficient text classification and representation learning method developed by Facebook's AI Research (FAIR) lab. IndoBERT is a pre-trained language model specifically designed for the Indonesian language, leveraging the Bidirectional Encoder Representations from Transformers (BERT) architecture. By analyzing a total of 119,310 data from January 2020 to June 2023, the tweets data were categorized into negative, neutral, and positive classes. Model yielded the highest accuracy of 82.5% using IndoBERT method. The results outcomes positive perceptions of electric vehicles among Indonesian citizen with a percentage of 58%. By carrying out this research, it is hoped that it can produce quality information for producers, the community and the government in developing and advancing public interest in purchasing electric vehicles considering the very positive impact they have on the surrounding environment.

In the era of increasing demand for sources of renewable and cleaner energy, electric vehicles offer possible solutions in order to maintain and improve the mobility of transport systems. In parallel, the application of machine learning for digital twin technology greatly contributes to the development and optimization of vehicles and systems, saving time and resources, as well as material resources. In terms of electric vehicle components, electric batteries represent the most expensive elements where machine learning can help to optimize characteristics during exploitation and to predict maintenance time and their lifetime. This article related to the possibilities of future research, which, by intensifying the digitalization and machine learning for digital twin technology, will affect the improvement of the application and disposal of components, but the complete system of electric vehicles, during the entire life cycle, including the recycling.

Abstract In this work, a new kind of charge scheduling algorithm is proposed by utilizing the War Strategy Optimization (WSO) algorithm. The strategies used in the war such as attack, defense, assigning soldiers to take positions are the inspiration to this algorithm. The proposed WSO algorithm is validated in a constructed geographic area which consists of Six starting/destination points, sixteen nodes, and twelve charging stations. In terms of waiting time and charging cost, the experimental results show that the WSO method much improves over current methods. The average waiting time and average charging cost of EVs are validated in MATLAB, with different considerations such as different number of EVs varied from 25 to 100, and different number of charging piles varied from 1 to 4. The WSO algorithm specifically lowered charging costs by up to 13.67% compared to the same and waiting time by up to 83.25% relative to the First Come First Serve algorithm. Comparatively to the Chaotic Harris Hawk Optimization and Harris Hawk Optimization algorithms, the WSO method demonstrated declines in waiting time by 11.17% and 39.09%, respectively, and declines in charging costs by 3.61% and 12.45%, respectively. Especially in situations with limited charging infrastructure, these findings show that the WSO algorithm may improve the efficiency and cost-effectiveness of EV charging management systems. For real-world EV charging management systems, the method's capacity to efficiently allocate EVs among charging stations, lower waiting times, and lower charging costs makes it a potential solution.

Abstract This study presents a novel Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFFOPID) controller for enhancing the speed control performance of permanent magnet synchronous motor (PMSM) drives in Electric Vehicles. The proposed GA-AFFFOPID controller, which combines the advantages of genetic algorithm optimization and adaptive fuzzy fractional-order PID control, represents a unique and innovative approach to address the control challenges associated with PMSM drives. Permanent magnet synchronous motor technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in electric vehicle drive systems. However, the inherent non-linearity, dynamics, and uncertainties of permanent magnet synchronous motors pose significant control challenges. The exceptional performance of the GA-AFFFOPID controller, demonstrated through its superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances, underscores the significant advancements enabled by the genetic algorithm optimization technique in improving the control performance of PMSM drives for electric vehicle applications. Comparative analysis with traditional control methods demonstrates the exceptional performance of the Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative controller. These findings highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications.

The advantages of fast charging speed and high efficiency of DC charging piles make the connection and cutting of electric vehicle (EV) loads relatively frequent. However, as a DC system, DC charging pile has the characteristics of low inertia and weak damping. In order to reduce the impact of EV load connection and cutting on the DC side voltage disturbance, a DC charging pile control method based on improved virtual inertia (VI) control is proposed. This paper firstly introduces VI control by analogy with virtual synchronous machines (VSM) technology. Secondly, the VI control is improved by using command filter backstepping integral sliding mode (CFBISM) control, in which the integral sliding mode (ISM) control is used to optimize the VI control to enhance the robustness of the system, and the command filter backstepping control is used to reconfigure the key parameters to avoid the computational inflation of the controller, while the ISM control is used to improve the current inner loop to further enhance the control effect. The stability of the proposed control strategy is then demonstrated by the Lyapunov stability criterion. Finally, through comparative simulation, it is verified that the DC-side voltage fluctuation can be limited within 2 V, the dynamic response speed is improved by about 0.1 s, and the perturbation of the key parameters in the steady state is limited to about 1 V, thus the superiority of the fast response speed and good robustness of the control proposed in this paper are verified by simulation.

Electric vehicles (EVs) as a sustainable safety system are being increasingly used and receiving attention from researchers for several reasons including optimal performance, affordability for consumers, and environmental safety. EV speed control is a crucial issue that requires reliable and intelligent controllers for maintaining this matter. The primary goal of this research is to design the linear and nonlinear Proportional, Integral, and Derivative (PID) controllers to control EV speed based on the minimum value of ITAE plus ISU (integral square of control signal) as well as satisfy the constrain on response overshoot. All the proposed PID controllers, conventional PID controller, arc tan PID controller, and nonlinear PID controller (NL-PID) are used in cascade with EV model. In all these PID controllers, a filter is used with the derivative term to avoid the effect of the noise. The tuning of the proposed controller gains is achieved using Aquila Optimization algorithm. The controllers’ parameter tuning is primarily determined by reducing the Integral Time Absolute Error (ITAE) and integral square control signal. Numerical simulation, system modelling, and controller design are done using MATLAB. By comparing the results, the proposed controllers’ efficacy is demonstrated. The proposed NL-PID controller provides promising EV speed regulation control and robustness to external disturbances. Where, the performance specifications of the proposed NL-PID controller when using unit step input with step disturbance of 0.2 and −0.2, and the model parameters increased by 25% from its nominal value are the rise time is 2.608, settling time is 2.608, overshoot is 0.073%, the maximum control signal is 6.230, number of slope sign change in the control signal is 231, and ITAE is 17.6064. The comparative results show the NL-PID controller’s superior performance for tracking the reference signal with the lowest peak for the control signal, rejection of disturbances, ability to overcome model uncertainties, and lowest ITAE value.

The article is devoted to the study of dynamic processes in the power unit of electric vehicle when shifting gears in a two-shaft gearbox by means of a cone synchronizer. A two-mass dynamic model is used to obtain analytical dependences. This model of the power unit makes it possible to obtain a qualitative idea and a quantitative assessment of the electric vehicle gearshift process, to select parameters of the laws of engine torque and synchronizer friction moment change. For a more precise description of vibrations in a power unit, taking into account its elastic properties, a three-mass dynamic model is considered. An example of calculating the time of shifting from low gear to top one for the MAZ-4381EE delivery electric truck is given. The studies conducted have shown that the vibrational processes occurring in the power unit of electric vehicle do not significantly affect the dynamic loading of its elements.

The detection system of DDoS (Distributed Denial-of-Service) attacks aims to enhance network security across all facets of internet technology utilization. One is at SPKLU, which stands for Public Electric Vehicle Charging Station. The research employed a deep learning approach utilizing a Convolutional Neural Network (CNN) on a publicly available dataset. Based on our study and analysis, CNN has a precision rate of 95%. Its high accuracy and balanced performance across diverse attack types indicate the model's practical application in real-life situations. The model demonstrates promising performance in detecting different network traffic anomalies, offering significant insight into its potential for practical use. Further investigation is necessary to strengthen the resilience of DDoS assault tactics against emerging dangers and to tackle any potential constraints.

(1) Introduction: Transportation logistics play a pivotal role in facilitating both individual mobility and supply chain operations on a global scale. However, conventional transportation systems have contributed significantly to urban congestion and environmental degradation. In response to these challenges, there is growing momentum to investigate the potential of renewable energy to power electric vehicles (EVs) and flying automobiles, aiming to reduce fossil fuel dependence and carbon emissions. (2) Objectives: By analyzing key drivers and opportunities for integrating flying vehicles into existing infrastructure through dynamic modeling, this study seeks to accelerate the transition to sustainable transportation. (3) Methodology: A mixed-method approach, combining literature review and causal loop diagram analysis, is employed to understand the factors influencing EV and flying car adoption. (4) Results: Findings highlight the significant potential of renewable energy in reducing emissions and reliance on fossil fuels through widespread EV and flying vehicle adoption. The major drivers and challenges in infrastructure, safety, and airspace management are also identified. In addition, this research emphasizes the importance of sustainable transportation in addressing urban congestion, pollution, and energy security. (5) Conclusions: By leveraging renewable energy and embracing technological advancements, a low-carbon future for transportation can be achieved, benefiting both the environment and society.

Abstract Lithium-ion (Li-ion) battery has played a key role for the development of electric vehicle (EV) at present, while the Li-ion batteries in the market come from different manufactures. Verifying the performance of the battery management system (BMS) for various battery chemistries is a complex undertaking. This paper proposes a high-fidelity Li-ion battery emulator for EV applications. The emulator utilizes a battery model parameterized by a series of performance tests and a special-designed hardware platform. A three-order battery equivalent circuit model (ECM) is selected to provide the voltage and current reference signal in the processor. Subsequently, the hardware generates the high voltage and current signal in accordance with the reference. To ensure the high accuracy of the battery ECM, a 37 Ah nickel manganese cobalt (NMC) battery was selected for testing under both charge and discharge conditions, as well as across a temperature range of − 30℃ to 45℃. The battery emulator is verified on charge and discharge mode for both accuracy and dynamic performance validations.

A massive market penetration of electric vehicles (EVs) associated with nonnegligible energy consumption and environmental issues has imposed a big challenge on evaluating electrical power distribution and related transportation facilities improvement in response to the large-scale EV charging service need. Strategical deployment of EV charging stations including location and determination of number of slow charging stations and fast charging stations has become an emerging concern and one of the most pressing needs in planning. This paper conducts a comprehensive survey of EV charging demand and distribution models with consideration of realistic driver behaviors impacts. This is currently a shortage in academic literature, but indeed has drawn practical attention in the strategic planning process. To address the need, this paper presents an in-depth literature review of relevant studies that have identified different types of EV charging facilities, needs or concerns that are considered into EV charging demand and distribution modeling, alongside critical impacting factor identification, mathematical relationships of the contributing factors and EV charging demand and distribution modeling. Key findings from the current literature are summarized with strategies for optimized plan of charging station deployments (i.e., location and related number of charging station), in an attempt to provide a valuable reference for interested readers.

This study presents the hierarchical structure of 50 sub-criteria divided into 7 main criteria for the assessment of electric vehicle (EV) deployment. Two options, Average Rank Transformations and Analytic Hierarchy Process methods, were applied in determining the local weights of the sub-criteria. The sufficient compatibility of expert opinions was accomplished using the averages of the ranks of the main criteria and sub-criteria as the result of solving the problem. The averages of the local weights were calculated employing three Multiple Criteria Decision-Making methods that increased the reliability of the research results. Based on this, the global weights and priorities of the sub-criteria were evaluated. The experts suppose that EV deployment at the national level is mainly affected by the higher cost of manufacturing and purchasing EVs, the application of financial incentives for purchasing EVs, the lack of exhausted gasses, the installation of fast charging points, and the absence of infrastructure in the five largest cities nationwide. The obtained results demonstrate that out of 50 sub-criteria, the cumulative global weight of the 10 most important sub-criteria (mainly based in economics) amounts to more than 35%, whereas that of the 22 most important sub-criteria have a weight above the average (0.2), reaching approximately 65%. The findings can be put into practice by state decision makers of EV deployment.

Abstract Taking a rear‐wheel drive pure electric vehicle as the research object, considering the safety during braking and improving energy recovery rate, a study was conducted on the distribution strategy of front and rear axle braking force. During the braking process, the feedback braking force of the motor and the hydraulic braking force with an electronic stability controller (ESC) were coordinated and controlled, to ensure that the total required braking force was met. A fuzzy logic controller has been designed, with three variables of battery state of charge, vehicle speed, and braking intensity as inputs, and a modified motor braking ratio as output variable to prevent wheel lockup. Using Cruise software, a co‐simulation model was established with Amesim and Simulink, and simulation validation was conducted on the braking process and cycling conditions. The simulation results showed that the brake recovery strategy based on fuzzy control can effectively improve the vehicle's control performance and energy recovery rate compared to the Economic Commission of Europe regulation. The NEDC (New European Driving Cycle) working condition improved by 10.41% and the CLTC‐P (China Light‐duty Vehicle Test Cycle‐passenger) working condition improved by 10.57%. Effectively improving power consumption per 100 km, NEDC decreased by 1.81% and CLTC‐P decreased by 2.62%.

Electric vehicle (EV) is more environmental-friendly than gasoline vehicle because the main power is not petrol. For the heating system, the waste heat generated by engines is recycled to heat the cabin of gasoline ones, while the EV need extra electric energy consumption for heating the cabin which decreases the driving range. Personal comfort system (PCS) has been reported that it is an alternative technique to improve occupant thermal comfort and indirectly save building heating energy. This study aims to investigate the occupants’ thermal sensation under four heating modes in the EV cabin in winter, including no heating, only PCS, only air conditioning, and both air conditioning and PCS. The field survey is conducted in a parked EV with 12 subjects. Three thermal comfort indexes (PMV, SET*, and operative temperature Top) are used and assessed for their applicability in the thermal environment of the EV. Results show that the thermal environment in the cabin is non-uniform and dynamic with the air conditioning mode, while the PCS mode just slightly affects the temperature differences inside. Meanwhile, the subjects’ thermal sensations cannot maintain neutral or warmer under the PCS mode only. The results of PMV are lower than the subjects’ actual thermal sensation votes (TSV), while the results of SET* and Top show significant linear relationships with the TSV for the correlation coefficients above 0.80. Compare to the air conditioning mode, the neutral SET* and neutral Top are decreased by 2.7 °C and 1.6 °C under the mode with PCS and air conditioning, respectively. The findings indicate that PCS not only has a positive effect on improving the cabin environment and occupant's thermal sensation but also has the potential to lower the setting temperature of air conditioner of EVs in winter.

Problem. The relevance of this work is explained by the fact that there are no works at all in the reference and normative literature on the theory and practice of forensic auto technical examination, and even more so, methodical recommendations regarding the values of the braking parameters of vehicles of the M1 category with an electric power plant. Goal. The purpose of the work is to establish the factors that affect the braking parameters of M1 category vehicles with an electric or hybrid power unit. Methodology. The approaches adopted in the work to solve the set goals are based on the theory of conducting the experiment, the theory of the interaction of the pneumatic tire with the road surface, the theory of the car and the laws of theoretical mechanics. Results. On the basis of the obtained experimental data regarding the value of permanent deceleration and the time value of the increasing deceleration during emergency braking of the M1 category vehicles with an electric or hybrid power unit, the tasks will be solved necessary for conducting forensic engineering and transport examinations for specific road traffic events with the participation of M1 category vehicles with an electric or hybrid power unit. Originality. The results of the study provided an opportunity to get an idea of the influence of factors on the braking parameters of M1 category vehicles with an electric or hybrid power unit. Practical value. The obtained results can be recommended to forensic experts when writing expert opinions or expert studies.

Electric vehicles (EVs) cut greenhouse gas emissions and our use of non-renewable resources, making them more attractive. EVs have lower fuel and maintenance expenses than internal combustion engine automobiles. This study proposes a multi-converter/Multi‒Machine system with two induction motors (IM) that drive a pure EV’s rear wheels. EV two-stage controllers using a simple Adaline neural network (NN) regulate Field-Oriented regulate of a three-phase IM. To control IM speed, the first controller level is a hybrid proportional–integral (PI) with a robust integral sign of error (RISE) controller. Injection torque is controlled by PI‒adaline NN in the second controller step. The simple Adaline NN improves two-stage controller performance. The Multi-Verse Optimization algorithm found the ideal RISE parameter to improve EV drive system performance. A plug-in EV’s linear speed is controlled by the Electronic Differential Controller (EDC). It uses the driver’s reference speed and steering angle to set each driving wheel’s reference speed. EDC adjusts wheel speeds to enhance traction and stability during cornering, accelerating, and decelerating. Utilizing this information, the EDC can effectively distribute power and torque to the wheels, thereby enhancing vehicle handling and overall performance. Three distinct road scenarios and the designated driving route topology have been used to act and demonstrate the resistive forces that affected the EV while it was traveling down the road. By using Matlab (Simulink), EV’s roadworthiness and efficiency will be evaluated.

Abstract This work develops a dual-layer energy management (DLEM) model for a microgrid (MG) consisting of a community, distributed energy resources (DERs), and a grid. It ensures the participation of all these energy entities of MG in the market and their interaction with each other. The first layer performs the scheduling operation of the community with the goal of minimizing its net-billing cost and sends the obtained schedule to the DER operator and grid. Further, the second layer formulates a power scheduling algorithm (PSA) to minimize the net-operating cost of DERs and takes into account the load demand requested by the community operator (COR). This PSA aims to achieve optimal operation of MG by considering solar PV power, requested demand, per unit grid energy prices, and state of charge of the battery energy storage system of the DER layer. Moreover, to study the impact of electric vehicles (EVs) load programs on DLEM, the advanced probabilistic EV load profile model is developed considering practical and uncertain events. The EV load is modelled for grid to vehicle mode, and a new mode of EV operation is introduced, i.e., vehicle to grid with EV demand response strategy (V2G_DRS) mode. The solar PV and load demand data are obtained from the MG setup installed and buildings present at the university campus. However, a scenario reduction technique is used to deal with the uncertainties of the obtained data. In order to evaluate the efficacy of the developed DLEM, its results are compared to previously reported energy management models. The results reveal that DLEM is superior to the existing models as it decreases the net-billing cost of COR by 13% and increases the profit of the DER operator by 17%. Further, it is found that for the highest EV penetration, i.e., 30 EVs, the V2G_DRS mode of EV operation reduces the total energy imported by COR by 11.39% and the net-billing cost of COR by 7.88%. Therefore, it can be concluded that the proposed model with the introduced V2G_DRS mode of EV makes the operation of all the entities of MG more economical and sustainable.

We consider the scheduling of battery charging of electric vehicles (EVs) integrated with renewable power generation. The increasing adoption of EVs and the development of renewable energies contribute importance to this research. The optimization of charging scheduling is challenging because of the large action space, the multi-stage decision making, and the high uncertainty. To solve this problem is time-consuming when the scale of the system is large. It is urgent to develop a practical and efficient method to properly schedule the charging of EVs. The contribution of this work is threefold. First, we provide a sufficient condition on which the charging of EVs can be completely self-sustained by distributed generation. An algorithm is proposed to obtain the optimal charging policy when the sufficient condition holds. Second, the scenario when the supply of the renewable power generation is deficient is investigated. We prove that when the renewable generation is deterministic there exists an optimal policy which follows the modified least laxity and longer remaining processing time first (mLLLP) rule. Third, we provide an adaptive rule-based algorithm which obtains a near-optimal charging policy efficiently in general situations. We test the proposed algorithm by numerical experiments. The results show that it performs better than the other existing rule-based methods.

In order to coordinate the interests between electric vehicle owners and the active distribution network, a multi-objective hierarchical optimization method for the optimal scheduling of the active distribution network with electric vehicles is proposed. This method takes into account both the comprehensive satisfaction of electric vehicle charging and the operational benefits of the active distribution network. The upper layer model focuses on maximizing the charging benefits of electric vehicle owners, and the normalized normal constraint method is used to solve the optimal charging and discharging plan of electric vehicle, which is input to the lower layer optimization model. The lower layer model aims to maximize the operational efficiency of the active distribution network by adjusting the output power of the controllable distributed generation according to the charging and discharging plan of electric vehicle. The second-order cone program and weighted minimal modular ideal point method are used to solve the nonlinear multi-objective problem. Simulation results show that the proposed multi-objective hierarchical optimization method can promote the comprehensive satisfaction of electric vehicle charging exceeding 0.9 while reducing active power loss by 94.12% and operating costs by 30.90%, which achieves a win-win situation for both electric vehicle owners and the active distribution network.

Abstract Accurately estimating Battery State of Charge (SOC) is essential for safe and optimal electric vehicle operation. This paper presents a comparative assessment of multiple machine learning regression algorithms including Support Vector Machine, Neural Network, Ensemble Method, and Gaussian Process Regression for modelling the complex relationship between real-time driving data and battery SOC. The models are trained and tested on extensive field data collected from diverse drivers across varying conditions. Statistical performance metrics evaluate the SOC prediction accuracy on the test set. Gaussian process regression demonstrates superior precision surpassing the other techniques with the lowest errors. Case studies analyse model competence in mimicking actual battery charge/discharge characteristics responding to changing drivers, temperatures, and drive cycles. The research provides a reliable data-driven framework leveraging advanced analytics for precise real-time SOC monitoring to enhance battery management.

Electric vehicles have seen substantial growth in recent years in the emerging economies of the world. The current market scenario of E-vehicles is dominated by PMSM or BLDC motors due to their higher torque-to-power density, and efficiency with added performance metrics of the motor. However, the higher capital cost of permanent magnets makes the motor designers to consider an alternative to magnetic motors, the so-called Switched Reluctance Motors (SRM). With proper FEM analysis and controller design, if SRMs can able to achieve the performance metrics of a PMSM/BLDC, it will be a breakthrough in the automotive sector with affordable prices. Based on the aforementioned facts, this research work focuses on SRM as a viable alternative to the existing PMSM/BLDC motor. The work concentrates on the design and development of an 8/6 SRM for a Tri-wheeler E-vehicle application. The designed motor is fabricated, tested and controlled by WAVECT, an FPGA-based real-time digital controller which is used to control the commutation of the 8/6 SRM. The yielded response satisfactorily proves that the designed 8/6 SRM can be effectively used for the Tri-wheeler E-vehicle application, thereby paving the way for sustainable energy conservation in the automotive sector.

The rapid expansion of the electric vehicle (EV) market presents a paradox: while increasing production and lowering costs are essential for widespread adoption, these efforts also intensify the environmental and social impacts, particularly in lithium-ion (Li-ion) battery production. Comprehensive sustainability assessments are needed across all stages of battery production. This review employed the ROSES framework to analyze 40 Scopus-indexed research papers systematically. Extracted indicators are categorized by the STEEP (Sociocultural, Technological, Economic, Environmental, and Political-Legal) dimensions. The dual approach identifies critical sustainability gaps and examines the interplay between these dimensions. By mapping each indicator to a specific lifecycle stage—ranging from raw material extraction to end-of-life disposal—the review highlights critical stages for improving sustainability in EV battery production. The study uncovers complex relationships between sustainability factors and offers a comprehensive framework for addressing these challenges. It provides a comprehensive framework for policymakers, industry leaders, and researchers seeking to develop more informed strategies for improving the sustainability of EV battery production.

This study presents a novel methodology to enhance the energy efficiency of Electric Vehicles (EVs) while maintaining dynamic stability and driving comfort, even on uneven roads, using onboard vehicle measurements. The approach involves a hierarchical control scheme that integrates a model predictive controller with a torque vectoring algorithm to optimize torque demand and accurately anticipate road-specific torque requirements. The proposed control is designed and implemented on an EV with Interior Permanent Magnet Synchronous Motors (IPMSM) on four wheel drives. The optimal torque control is realised through an Artificial Neural Network (ANN) - based motor torque control scheme. The design is validated through tests in real-world driving scenarios. In comparison with conventional methods, the proposed method shows a 37% increase in energy efficiency across different test conditions, thereby resulting in an increase in EV driving range. These advancements are realised without substantial modifications to the EV’s drivetrain, representing a significant step forward in sustainable and efficient electric mobility.

The depletion of conventional energy sources coupled with the rising demand for electric vehicles (EVs) has significantly underscored the necessity for electric vehicle supply equipment (EVSE) with advanced energy supply management within microgrids. Effective energy management of EVs and EVSEs is imperative to satisfy EV owners and stabilize microgrids. This paper introduces multi-objective grey wolf optimization (MOGWO) to optimize EVSE and EV charging costs. MOGWO achieves substantial cost savings through dynamic pricing based on time of day, state-of-charge and hour-based scheduling which promotes off-peak charging thereby enhancing system efficiency and reducing costs. The algorithm also provides flexibility for trip interruptions and outperforms other optimization algorithms in minimizing EV charging/discharging costs by achieving reductions of up to ₹488 and obtaining average grid efficiency up to 76.41 % during charging and 87.41 % during discharging. Integrating Vehicle-to-Grid and Grid-to-Vehicle systems enhances microgrid resilience and delivers economic benefits to EV owners. The cost optimization for EV charging/discharging has been executed using MATLAB 2023a software to determine the most cost-effective charging paths by considering energy availability and grid conditions. The evolving sustainable energy landscape combined with MOGWO paves the way for smarter and more resilient microgrids in the era of electrified transportation.

The rapid growth in the popularity of Electric Vehicles (EVs) requires accurate driving range predictions to minimize range anxiety and optimize trip planning, especially in real-world driving conditions where diverse factors affect range. This study addresses the challenges of EV range prediction by presenting a novel deep learning technique that uses a Deep Neural Network (DNN) model optimized with the RMSProp optimizer. This approach leverages a unique real-world dataset that reflects varied driving environments, leading to superior performance. The model achieves an R2 score of 0.99, a Mean Absolute Percentage Error (MAPE) of 2.01%, a Mean Absolute Error (MAE) of 6.81 km, and a Root Mean Squared Error (RMSE) of 9.32 km. These results significantly outperform conventional machine learning techniques like support vector machines and linear regression, demonstrating the practicality and reliability of the proposed model for reducing EV range anxiety and improving trip planning in real-world scenarios. This enhancement supports the broader adoption of EVs, ultimately contributing to a sustainable transportation ecosystem

Currently, the term battery-supercapacitor associated with hybrid energy storage systems (HESS) for electric vehicles is significantly concentrated towards energy usage and applications of energy shortages and the degradation of the environment. The explosion of chargeable automobiles such as EVs has boosted the need for advanced and efficient energy storage solutions. Battery-supercapacitor HESS has been introduced to meet these requirements because of the high energy density of batteries and the high-power density of supercapacitors. Subsequently, each storage technology indicates exceptional risks. Significantly, batteries, particularly lithium-ion, suffer from reduced lifespan and thermal runaway because of frequent charging cycles.Furthermore, supercapacitors, while providing high-power output and excellent cycle durability, are expensive and add complexity to the system. This review paper examines the recent progress in designing and incorporating HESS for EV applications. Some innovations comprise new materials for batteries specifically and supercapacitors in general, new concepts of their structure, enhanced power control systems, and control strategies based on the advanced integration of combinations of the two energy storage devices. Furthermore, this review paper examines the effects of integrated HESS on performance characteristics such as an enhanced driving range, better acceleration, and the battery's overall durability. Such pros and cons include cost, scalability, system complexity, possible options for ways forward, and directions for further extensive research. The study underlines the potential of using battery-supercapacitor hybrid systems to develop post-quarter EVs and electricity storage systems.

In recent years, electric vehicle (EV) battery-swapping technology has rapidly evolved and is expected to become widely prevalent shortly. Therefore, it is crucial to develop efficient battery-swapping scheduling algorithms to optimize the operations of battery-swapping systems. This paper proposes a non-cooperative game approach for the battery-swapping scheduling of EVs. To reduce the waiting time for battery swapping and improve the scheduling efficiency of EVs, a swapping process model inspired by the job-shop scheduling problem is proposed, and the cost function of each EV comprehensively considers the travel time, waiting time, and battery swapping price. To capture the competitive relationship among EVs, a non-cooperative game model for battery swapping scheduling is established considering the finite quantities of batteries and swapping grippers. To find the pure strategy Nash equilibrium, an iterative best response algorithm is developed, satisfying constraints including those couple decisions of different EVs. Case studies demonstrate the fairness and scheduling efficiency of the proposed approach.

This paper presents a two-layer optimal configuration model for EVs' fast/slow charging stations within a multi-microgrid system. The model considers costs related to climbing and netload fluctuations, aiming to meet EVs' charging demands while ensuring grid safety and economy. The upper layer focuses on minimizing investment and operational costs for the microgrid, accounting for EVs' charging characteristics and interdependencies among microgrids. It develops an optimal configuration model for charging stations across multiple microgrids and implements differentiated electricity pricing in various zones to promote orderly charging. The lower layer aims to minimize EVs' charging costs. Using differentiated pricing, it analyzes the spatiotemporal distribution of EVs' charging loads through the Monte Carlo method. This analysis informs the upper layer for further optimization of charging station configurations. An analysis of three scenarios shows that the proposed approach reduces EVs' charging costs by 44.3% compared to uncoordinated charging. It also mitigates the impact of EVs' charging loads on the microgrid, enhances operational safety and contributes to increased social welfare.

Among the different classes of electric vehicles (EVs), electric two-wheelers (E2Ws) are securing more enticement towards the commutator for regular usage due to their higher suitability in the heterogeneous traffic conditions of India. However, the driving range of the E2Ws is the major concern in increasing its penetration rate in road transportation. To eradicate the range anxiety of commutators by redesigning the current E2 W powertrain configuration, this study strives to insights into the operational limits of powertrain elements such as the battery and motor in E2 W under multiple operational circumstances such as driving style, pavement, and ambient conditions. Regarding this, the E2 W with data acquisition system (DAC) connected with an onboard diagnosis (OBD) port is utilized to acquire the data related to powertrain components and vehicles for four driving trips (trips 1–4) under three major road segments (Rural, expressway, and urban) of India. The operating and computed performance metrics, comprising battery voltage and current, state-of-charge (SOC), energy consumption, motor current, speed, efficiency, and so on, are then compared throughout driving trips and road segments. The mean velocity of E2 W for trips 1 to 4 is 31.91, 33.68, 39.03, and 35.58 km/h, respectively. The results disclosed that total energy consumption and energy consumption per km. are higher during trip 3 due to higher average vehicle speed. Also, it has a higher depth-of-discharge (DOD) of 83.91 % than other driving trips. Further, a higher rate of SOC drop, energy consumption and battery temperature rise are found at highway road segments.

This study investigates the impact of vapor injection parameters and positions on the performance of a low-pressure ratio scroll compressor in electric vehicle thermal management systems under extremely low temperatures. The research combines experimental and simulation methods to analyze five injection ports designed at different positions. Key performance metrics, including mass flow rate, discharge temperature (Tdis), coefficient of performance (COP), compression work, and heating capacity (Qh) were evaluated under various conditions. A low-pressure ratio (scroll number N = 2) vapor injection scroll compressor was designed with an optimized injection port configuration. This design was rigorously validated through experimental results, confirming its efficacy. Notably, the findings reveal that the enhancement in Qh and COP is more pronounced in extremely low-temperature working conditions compared to non-injection conditions, with improvements of 10.7 % and 4.6 %, respectively. Compressor performance increases with increasing vapor injection pressure, and compressor speed and performance increment are more significant under low-temperature working conditions. Finally, an injection coefficient, denoted as k, is proposed to determine the optimal injection pressure for diverse discharge and suction pressures in cold climates. According to the experimental results, the value of k associated with the best heating COP ranges between 0.65 and 0.85.

The increasing scale of electric vehicles (EVs) and their stochastic charging behavior have resulted in a growing coupling between the transportation network and the grid. Consequently, effective smart management in the EV-transportation network-grid integration system has become paramount. This paper presents a comprehensive review of the current state of the art in system modeling and optimization approaches for the smart management of this coupled system. We begin by introducing the types of EVs that impact the transportation and grid systems through their charging behavior, along with an exploration of charging levels. Subsequently, we delve into a detailed discussion of the system model, encompassing EV charging load forecasting models and transportation-grid coupling models. Furthermore, optimization technologies are analyzed from the perspectives of system planning and EV charging scheduling. By thoroughly reviewing these key scientific issues, the latest theoretical techniques and application results are presented. Additionally, we address the challenges and provide future outlooks for research in modeling and optimization, aiming to offer insights and inspiration for the development and design of the EV-transportation network-grid integration system.

This study investigates the energy consumption of electric vehicles (EVs) in real-world driving conditions, using in-vehicle sensor data and location tracking. The energy consumption of EVs is compared to that of an internal combustion engine vehicle (ICEV) under similar driving tests. Furthermore, an energy-centric life cycle assessment was conducted to assess the environmental impact of the transition to electrified transportation by reporting the carbon emissions of the EVs and ICEV. Since a large volume of driving data was acquired in the experiment, machine learning (ML) techniques are employed to interpret the obtained data and identify a key feature. ML algorithms can help develop predictive models that estimate energy consumption and explain the major factors that impact such consumption. The findings also offer valuable insights into energy consumption, emission, and the underlying factors that can inform policy discussion related to transport electrification and enhance energy efficiency. This research is significant in comprehending the potential of current EVs and their superiority over traditional vehicles. It is vital to facilitate the shift toward electric transportation, which can be driven by the results.

Electric vehicles (EV) have increased in the last few decades due to their ability to reduce greenhouse gas emissions (GHG). Support for the electrification of the transportation sector has encouraged researchers to investigate the optimal placement of fast charging stations (FCS). In this study, we conducted a systematic literature review of 84 primary studies between 2019 and 2024 by identifying objective function and solution techniques, uncertainty, stakeholders, and network classification. We identified the objective functions most commonly used by authors related to technical and cost-solving problems using techniques: conventional (41.7%), metaheuristic (33.3%), hybrid (22.6%), and other (2.4%). Several researchers have also considered various uncertainty parameters from EV, FCS demand, and distributed generation (DG) power output with the most popular probabilistic method to solve problems. Furthermore, the role of stakeholders and network classification is also reviewed in this article. Our study contributes to the field by providing a comprehensive overview of the most significant journals and highlighting future research on the optimal placement of FCS. Future work must focus on improving parameters, models, methods, and using real data from various factors related to FCS demand.

This study investigates the factors influencing electric vehicle (EV) adoption in both developed and developing nations, with a particular focus on Indonesia's national strategy for EV promotion as mandated by Presidential Regulation Number 55/2019. Despite the Indonesian government's efforts, EV market penetration remains minimal, accounting for

In this paper, we develop a profit-sharing-based optimal routing mechanism to incentivize horizontal collaboration among urban goods distributors. The core of this mechanism is based on exchanging goods at meet points, which is optimally planned en route. We propose a Collaborative Electric Vehicle Routing Problem with Meet Points (CoEVRPMP) considering constraints such as time windows, opportunity charging, and meet-point synchronization. The proposed CoEVRPMP is formulated as a mixed-integer nonlinear programming model. We present an exact method via branching and a matheuristic that combines adaptive large neighborhood search with linear programming. The viability and scalability of the collaborative method are demonstrated through numerical case studies, including a real-world case and a large-scale experiment with up to 500 customers. The findings underscore the significance of horizontal collaboration among delivery companies in attaining both higher individual profits and lower total costs. Moreover, collaboration helps to reduce the environmental footprint by decreasing travel distance.

The widespread adoption of electric vehicles (EVs) underscores the urgent need for innovative approaches to estimate their lithium-ion batteries’ state of health (SOH), which is crucial for ensuring safety and efficiency. This study introduces SOH-TEC, a transformer encoder-based model that processes raw time-series battery and vehicle-related data from a single EV trip to estimate the SOH. Unlike conventional methods that rely on lab-experimented battery cycle data, SOH-TEC utilizes real-world EV operation data, enhancing practical application. The model is trained and evaluated on a real-world dataset collected over nearly three years from three EVs. This dataset includes reliable SOH labels obtained through periodic constant-current full-discharge tests using a chassis dynamometer. Despite the challenges posed by noisy EV real-world data, the model shows high accuracy, with a mean absolute error of 0.72% and a root mean square error of 1.17%. Moreover, our proposed pre-training strategies with unlabeled data, particularly SOH ordinal comparison, significantly enhance the model’s performance; using only 50% of the labeled data achieves results nearly identical to those obtained with the full dataset. Self-attention map analysis reveals that the model primarily focuses on stationary or consistent driving periods to estimate SOH. While the study is constrained by a dataset featuring repetitive driving patterns, it highlights the significant potential of transformer for SOH estimation in EVs and offers valuable insights for future data collection and model development.