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

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

Addressing the need for multiple health indicators is critical to improving prediction accuracy and reducing the limitation of reliance on a single health indicator. This paper presents an AutoML model for accurately forecasting the life span of lithium-ion batteries (LIBs) in electric vehicles. Unlike previous studies focusing solely on constant current (CC) and constant voltage (CV) charging, our AutoML approach includes ICA and direct analysis to generate eight novel multi-dimensional health indicators. These novel comprehensive indicators characterizing battery aging, including the position values of CC charging (IC_CC_P), the peak of CV charging (IC_CV_ H), and the peak/position of CC discharging (IC_D_H, IC_D_ P), are chosen based on correlation analysis and combined with four health indicators from direct analysis, i.e., capacity, cycle, cluster, and checkpoint, to improve prediction accuracy. K-means clustering is employed to group similar data points. At the same time, the ruptures library facilitates the implementation of a novel algorithm for change point detection, allowing for the identification of checkpoints within each battery dataset. Experimental validation using NASA's AMES LIB cycle life datasets demonstrates a significant performance improvement from existing recent methods, with 58.93 % lower mean absolute error (MAE) and 52.66 % lower root-mean-square error (RMSE) compared to recent AutoML-based methods. This shows a significant improvement in forecasting the RUL of electric vehicle LIBs using the proposed AutoML-based approach with novel multi-dimensional health indicators. [ABSTRACT FROM AUTHOR]

• A tripartite evolutionary game model of participation in recycling is constructed. • The government does not provide enough incentives for consumers. • In this recycling system, the government will gradually reduce its regulation. • The regulation of power battery recycling by the Chinese government is imperfect. • Improving compensation can improve consumers' recycling enthusiasm. While electric vehicles (EVs) are developing at a high speed in China, the power battery market is facing a decommissioning peak. The problem is that the recycling situation of domestic power batteries is not ideal, partly due to neglect by consumers. By considering the recycling system, mode, and policy of China's EV power batteries, we construct a tripartite evolutionary game model of the government, consumers and EV manufacturers; analyse the stable strategy adjustment mechanisms of tripartite participation in this recycling cooperation game; and simulate the tripartite evolutionary game. The results show that when the initial willingness of the government, consumers and EV manufacturers to recycle power batteries is not strong, the government takes the lead, driving EV manufacturers and consumers to participate in power battery recycling. When the government, consumers and EV manufacturers have medium or high levels of initial willingness, the government evolves and chooses a nonregulation strategy. In addition, by simulating the impact of changes in consumer-related influencing factors on this tripartite evolutionary game, we find that subsidies for recycling power batteries are a key factor affecting consumers' strategy choices and that boosting recycling compensation for consumers can improve their enthusiasm to participate in such recycling. Therefore, to improve the recycling of power batteries for EVs, in terms of both efficiency and percentage of deployment, the Chinese government should strengthen public education on power battery recycling, further integrate informal recycling channels, and balance the distribution of profits among consumers for recycling compensation. [ABSTRACT FROM AUTHOR]

Frequency control in a microgrid (μG) is problematic due to low inertia of distributed generators (DGs) and uncertainty of the renewable energy source-dependent DGs. Consequently, deploying distributed storage units (DSUs) in a μG is crucial for quickly arresting frequency deviations. The electric vehicle (EV) technology, as DSU, is being preferred over conventional energy storage due to its lower cost and slower degradation rate, facilitating demand side response in a μG. The adoption of the EV technology necessitates an open communication infrastructure in the μG, with a pre-existing communication time delay (CTD). This article explores the impact of EV integration on load frequency control (LFC) performance of a multi-microgrid (MμG) system and determines an optimal CTD value simultaneously. A cyclical parthenogenesis algorithm-optimized 2DOF-FOPID controller is implemented to obtain dynamic responses of the MμG system. Simulation results reveal significant improvements in dynamic responses when integrating EVs in the MμG, with a 97.33% increase in objective function value. The recommended controller also outperforms standard controllers in terms of how quickly frequency and tie-line power variations settle down to zero. Thus, the proposed controller meets the LFC requirements. Robustness of the proposed LFC scheme is tested against parametric variations in the MμG system. [ABSTRACT FROM AUTHOR]

To increase the energy-absorbing capability of frontal collision management systems and improve vehicle crash safety, foam-filled crash boxes should be optimized. On the basis of a double tubular construction, a novel foam-filled crash box with a different design is developed. The energy absorption capacity, initial peak force, and deformation modes of the original and improved crash boxes were examined using impact models. As opposed to the full-filling design, it is demonstrated that the filling design may utilize less foam while increasing specific energy absorption. The stability of continuing deformation after the first buckling is determined by the foam-filled crash box. For the foam-filled crash box, a better-optimized design technique is suggested using the Taguchi method and principal component analysis (PCA). Compression tests are used to validate the design concept. Therefore, the optimal design technique of the crash box is suitable and practical for the crashworthiness design of crash boxes, considering the combined effect of significant indicators for electric vehicles. [ABSTRACT FROM AUTHOR]

In addressing the steering stability issues of four-wheel-drive electric vehicles on surfaces such as wet, slippery, frozen, and soft terrains, a novel control method based on particle swarm optimization for neural networks is proposed in this study. The approach integrates the advantages of Proportional-Integral- Derivative control, particle swarm optimization, and neural networks. By constructing a neural network model with input, hidden, and output layers, the study introduces particle swarm optimization algorithm for weight and structure optimization. Fuzzy logic and slip control theory are integrated into the steering stability control. The results demonstrated that, under wet and slippery road conditions, the model exhibited a system response time of 15 ms with a steering prediction accuracy of up to 92%. On frozen road surfaces, the model showed a system response time of 18 ms, with a steering prediction accuracy reaching 90%. Compared to other models, it significantly demonstrated superior steering stability control. This suggests that the designed model performs well in handling complex driving environments, indicating high application potential in the field of electric vehicle steering stability control. [ABSTRACT FROM AUTHOR]

The DWCS (dynamic wireless charging system) is an innovative electric vehicle charging solution that allows for charging while the vehicle is in motion without requiring direct physical contact. This groundbreaking technology has the capacity to significantly reduce the size of electric vehicle batteries and improve battery life if the DWCS is installed correctly along the route. However, using smaller batteries and improving battery life requires more DWCS stations to be installed along the route. This becomes more challenging due to the heterogeneity of battery vehicles, making finding the optimal compromise between batteries cost and charging infrastructure cost complex since each type has unique characteristics. To address this challenge, we present a quantitative analysis of the benefits of dynamic charging using a mathematical model that considers both batteries and charging infrastructure costs, considering the heterogeneity and life of battery vehicles. Our analysis focuses on identifying how much batteries cost can be reduced to decrease system costs while demonstrating the benefits of dynamic charging on battery life. In the resolution stage, we have created an innovative approach that utilizes multi-agent systems and reinforcement learning, explicitly Q-learning, within the framework of distributed artificial intelligence. [ABSTRACT FROM AUTHOR]

The topic of electric vehicles is currently generating significant interest worldwide. They are beneficial for the environment as they save up fuel, reduce pollution and carbon footprint too, but their market consumption is at the nascent level. The demand for these electric vehicles in developing countries is currently very slow and not that steady. The importance of Electric Vehicles (EVs) in the upcoming future is high and so it is crucial to figure out why these EVs are not being sold as much as we expect them in the market today. This paper tries to find out the multiple reasons why these vehicles are not in high demand, and why their counterparts, gasoline-fuelled vehicles are more in demand in the market. This paper discusses, after reviewing multiple other papers, why the consumption of EVs is low. Why their market demand is not where it should be and also multiple other facets. Finally, the paper finds that reasons like higher costs and low government incentives among multiple others are some factors leading to a low market demand for EVs. Also, the paper discusses the future scope of these EVs and how we can increase their consumption in the market. [ABSTRACT FROM AUTHOR]

This work presents a predictive torque vectoring controller that optimally monitors the vehicle limits of handling using active torque distribution in the rear axle of a fully electric vehicle. It works in combination with a feedforward controller designed to improve the vehicle's agility. The overall torque vectoring strategy is described together with the vehicle lateral dynamics, sideslip angle estimator, and torque allocation method. Numerical simulations for various scenarios and road profiles show the benefits of predicting the vehicle's handling limits and the enhancement of vehicle stability in terms of reduced vehicle sideslip angle and driver effort. The proposed optimal control method for predicting vehicle handling limit violations does not require a dedicated solver, making it a promising candidate for real-time applications. The case study is a vehicle equipped with two rear in-wheel motors in the framework of , an ECSEL Joint Undertaking (JU) European research project. Hardware-in-the-loop (HiL) tests were performed on a dedicated e-axle test bench to integrate the torque vectoring controller with the real e-motors and a dual inverter. The results of the HiL testing demonstrate that the torque-vectoring requirements are satisfied by the hardware configuration in use. [ABSTRACT FROM AUTHOR]

To promote electric vehicles, it is vital to know what impacts the preferences for electric vehicles over conventional fuel-based cars. To address this, a discrete choice experiment is developed and integrated into a survey. An online survey was conducted in Canada with 2062 valid responses. Different labels are designed for the survey to determine the most effective GHG information framing to increase the influence of such information on decisions. In this study, the influence of lifecycle emissions is considered. Three ensemble learning techniques are applied and they are compared based on prediction accuracy, and the most accurate technique is applied to determine the relative influence of variables on the intention to buy electric vehicles. Further, the interaction of variables is investigated using xgbfir. Subsequently, Accumulated Local Effect (ALE) is employed to examine the influence direction of top variables on the electric vehicle purchase likelihood. The results suggest that environmental attitudes and purchase price are the most influential parameters on the intention to buy electric vehicles. Moreover, those who are extremely worried about climate change, do not own a car, and self-identified as being at the top of the climate change stage of change are more likely to buy electric vehicles. • This study attempts to determine which variables influence the intention to buy EVs. • A discrete choice experiment is designed, and 2062 participants join the survey. • XGB is the most accurate method for predicting participant choices between PHEVs and ICEVs. • Environmental attitudes and price are the most influential parameters on EV preferences. • Leaves and Fire are the most efficient labels to increase the likelihood of BEV and PHEV purchase. [ABSTRACT FROM AUTHOR]

It has been suggested that increasing the use of electric vehicles (EVs) could help reduce global fuel use and greenhouse gas (GHG) emissions, two major contributors to climate change. The purpose of this research is to examine consumer attitudes on EVs and the factors that prevent their mainstream adoption. Unlike conventional survey methods, the researchers used theme analysis to uncover and make sense of the myriad of aspects that went into their purchase. The study also takes into account the often-overlooked part played by emotions. People's openness to express their opinions online makes eWOM a useful source of information for this study on electric automobiles. This technique is useful for identifying any and all gaps (physical and intangible) between a company's offering (EVs) and a customer's expectations. This research also sheds light on the interconnected nature of these obstructions, revealing a potentially crippling snowball effect on adoption rates. In this study, we look at how technology has altered transportation in the United States. Efficiency, sustainability, safety, and accessibility are only few of the topics covered, along with infrastructure, vehicle technology, and mobility services. This study analyzes data and research to provide light on how technology has changed the face of transportation in the United States. This research looked at how electric car market share relates to the availability of government subsidies and other important socioeconomic factors. The findings showed a negative correlation between energy prices and EV adoption, whereas a positive correlation between the market share of electric vehicles in each state and the presence of urban highways and government incentives. Based on the results of the sensitivity analysis, it appears that the cost of power has the greatest impact on the number of people opting to buy electric cars. The analysis of EV adoption found that the price of power and the availability of charging stations in metropolitan areas had the greatest impact. [ABSTRACT FROM AUTHOR]

In this study, an extended Interval-Valued Picture q-rung Orthopair Fuzzy Set (IVPq-ROFS) is developed, which indicates a trio-different combination of multi-granulated fuzzy numbers, and their basic operation and score function are discussed. An IVPq-ROFS was utilized for sustainability-focused transportation-sectorized electric vehicle battery charging technology selection problem among seven attributes and three alternatives. The application parameters are prioritized through innovative Integrated Objective and Subjective Weighted Approach (IOSWA) for attribute importance identification and Measurement of Alternatives and Ranking according to Compromise solution (MARCOS), which comes under Multi-Attribute Decision Making (MADM) model. This research paper elucidates efficiency of the proposed MADM approach under IVPq-ROFS environment and suggesting alternative battery swapping is best option for electric vehicle charging technology for more adaptation in society for future green mobility. The proposed computational decision algorithm results are validated through nine comparative and sensitivity cases, which proves the reliability and higher effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automotive Digest is the property of Automotive Digest Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The development of electric vehicles (EVs) has introduced novel technologies and manufacturing processes that expose workers to new risks of burn injury. We identified 6 patients who were admitted to our burn center for injuries that occurred while working in EV manufacturing facilities. The burns fell into 3 categories: flash flame burns due to lithium-ion battery explosions, high-voltage electrical injuries, and burns caused by contact with molten metal. Recognizing these recurrent patterns of injury should inform future prevention efforts and prepare health systems to evaluate and treat patients burned in EV manufacturing. [ABSTRACT FROM AUTHOR]

This article proposes a Fire Hawk Optimizer (FHO) technique for photovoltaic fed grid connected wireless electric vehicle battery‐charger. The optimization issues are solved by the FHO across a countless endless exploring space. The main aim of the proposed technique is to enhance the efficiency, reduce energy demand, improve communication amid the receiver and transmitter sides of electric vehicle (EV) and reduce range anxiety. A photovoltaic (PV) panel, an energy storage unit (ESU), and electric vehicles are part of the proposed topology. Each unit is separately regulated, and the converter of energy storage unit uses a voltage‐regulation mechanism to guarantee that the direct current bus voltage is kept in nominal‐level when operating in various circumstances. An essential requirement for the quick commercialization of EVs is the ability to charge them. Moreover, the charging‐station smartly uses grid power in the event that the battery storage is empty and the generation of solar photovoltaic array is not available. The inverter is tuned using the proposed technique. The FHO method is done in MATLAB software and it evaluated their performance. The proposed methodology provides higher efficiency of 91% than the existing techniques. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have been initiated as a preference for decarbonizing road transport. Accurate charging load prediction is essential for the construction of EV charging facilities systematically and for the coordination of EV energy demand with the requisite peak power supply. It is noted that the charging load of EVs exhibits high complexity and randomness due to temporal and spatial uncertainties. Therefore, this paper proposes a SEDformer-based charging road prediction method to capture the spatio-temporal characteristics of charging load data. As a deep learning model, SEDformer comprises multiple encoders and a single decoder. In particular, the proposed model includes a Temporal Encoder Block based on the self-attention mechanism and a Spatial Encoder Block based on the channel attention mechanism with sequence decomposition, followed by an aggregated decoder for information fusion. It is shown that the proposed method outperforms various baseline models on a real-world dataset from Palo Alto, U.S., demonstrating its superiority in addressing spatio-temporal data-driven load forecasting problems. [ABSTRACT FROM AUTHOR]

This research aims to employ the Autoregressive Distributed Lag (ARDL) method within the insight into the Environmental Kuznets Curve (EKC) to verify whether EKC exists in the Nepalese economy. In this research, variables were used, such as carbon emissions per capita, GDP per capita, energy use per capita, trade volume, and urbanization from 1980 to 2021, and the ARDL method was used. The data has been taken in this research except trade volume from the World Bank and the Ministry of Finance, Nepal. The data sets are converted into the natural logarithmic form to minimize the problem of heteroskedasticity. The findings provide compelling evidence for the existence of the EKC in Nepal, that economic growth has an inverted U-shaped impact on carbon emissions. In the early stages of development, economic growth leads to rising carbon emissions, but in the later stages, economic growth becomes associated with declining emissions. Besides economic growth, per capita energy consumption and urbanization emerge as significant drivers of carbon emissions. However, the trade volume is not found to be the driving factor of carbon emissions. The findings of this study have significant policy implications for global climate change issues and Nepal's transition from an underdeveloped to a developing nation. To achieve harmonious economic growth and emissions reduction, donor countries and agencies to partner with Nepal in its ambitious endeavors. This partnership can take shape through multifaceted support as fueling socio-economic progress that aligns with Nepal's commitment to reduce carbon emissions, ensuring that development and sustainability walk together. This research recommends the government of Nepal electrify the transportation landscape by incentivizing the adoption of electric vehicles, paving the way for cleaner air and a healthier planet, empowering Nepal's natural guardians by strengthening public and private forest programs, safeguarding invaluable ecosystems and biodiversity and curbing the tide of waste mismanagement through strict regulations and robust enforcement, transforming a potential threat into a source of innovation and resourcefulness. These measures, aligned with sustainable employment generation, can pave the way for a brighter and greener future for Nepal. [ABSTRACT FROM AUTHOR]

At present, electric vehicle (EV) development has been widely accepted as a coping strategy by countries encountering environmental issues. However, there remain some uncertainties surrounding the future of China's EV industry. The super-network provides an effective solution to the research on optimising subject decisions in a multilevel network model. On this basis, a closed-loop super-network model of the EV industrial chain system is proposed, which involves four different participants: EV manufacturer, sales company, consumer market, and recycling company. Furthermore, the variables are extracted based on practice, the behavioural strategies of each participant are analysed, and the dynamic equilibrium is determined with variational inequalities. Through numerical analyses, a further study is conducted on the main influencing factors and the extent of their impact on the dynamic equilibrium of the system. According to the results, the main research questions are answered, and some suggestions are made on the formulation of appropriate regulations. [ABSTRACT FROM AUTHOR]

By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and lowering energy bills. In this manuscript, a hybrid approach is proposed for smart grid home energy management with microgrids and day-ahead energy forecasts. The proposed control approach combines the Circle Search (CS) algorithm and Recalling-Enhanced Recurrent Neural Network (RERNN). Commonly it is named as CS-RERNN technique. The novelty of this paper is to optimise energy consumption and production within microgrids, thereby contributing to the overall efficiency of the smart grid system. The proposed method is used to reduce the electricity cost, peak-to-average ratio (PAR), and maximising consumer comfort. Energy management is performed based on the CS algorithm. A smart home connected to the external power grid (PG) is managed by the proposed method. Here, load demand is predicted by using RERNN. By then, the performance of the proposed method is implemented in MATLAB platform. The proposed method shows a high efficiency of 96%, and 22 $ of low electricity bill cost compared with other existing methods such as Particle swarm optimisation (PSO), Cuckoo search algorithm (CSA, and Border collie Optimisation (BCO). [ABSTRACT FROM AUTHOR]

Renewable energy sources are now being focused on for usage in electric vehicles. Although majority of electric vehicles run solely on batteries, they have eliminated local emissions but not pollution. To tackle this issue, this research looks into the performance and power management aspects of an electric vehicle's hybrid power system, with a particular emphasis on the interactions between fuel cells, batteries, and photovoltaic (PV) cells. Stable operation is ensured via a bi-directional buck-boost converter, which maintains the voltage from all sources around reference 430 V. Initially, the fuel cell supplies electricity under low light conditions. The PV system and battery take over when the irradiance surpasses 100 W/m2, with the battery first draining and then charging in tandem with the increase in irradiance. 1750 RPM is the constant motor speed that is reached after making the required torque adjustments. A model full electric vehicle with multiple sources has been proposed with an efficient power management algorithm. When the battery is in stationary mode and the state of charge (SOC) is less than 100%, extra solar power is used to charge the battery. The model also takes into consideration using the car for entertainment or gadget charging when the ignition is off. The biogas output can be regulated using an inferential control technique using artificial neural network (ANN) based on the fuel demand or consumption. The system also includes anaerobic digestion of organic waste to provide hydrogen for the fuel cell sustainably from biogas. Power, current, SOC profiles, motor performance graphs, and other validations show that the extensive simulations exhibit efficient power management and a strong model for hybrid electric car power systems. The model has been simulated in the MATLAB Simulink environment and has been tested under realistic conditions. • Proposed new powertrain strategy including PV-battery-PEMFC. • Analysed the heuristic power management for the proposed powertrain strategy. • Utilized biogas to produce Hydrogen for the Vehicle. • Utilized ANN for inferential control of hydrogen generation from biogas plant. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Systems are inherently tied to uncertainty, which necessitates the development of designs and schedules that accommodate this unpredictability. Particularly in modern power systems, the variable efficiency of numerous factors, including pricing, underscores the need for uncertainty modeling. In the meantime, the incorporation of EVs (electric vehicles) into the electrical grid is advancing, with these vehicles being recognized for their role in reducing dependency on fossil energies and enhancing the resilience, stability and efficiency of the grid. However, the rapid growth of the EV sector necessitates thoughtful strategies from decision-makers to manage its expansion effectively, particularly considering the environmental implications associated with the life cycle of EVs, including their charging and discharging processes. This research introduces the conceptual design and optimization approach utilizing a hybrid bat algorithm and differential evolution algorithm (BA-DEA) to improve the efficiency and resilience of EV-smart parking lots under the unpredictable conditions caused by grid pricing fluctuations within the DRP (demand response program). The technique effectively modulates daily costs by adjusting loads among peak and off-peak periods. Key features of the proposed approach include a non-dominated sorting model, innovative variable identification, a memory-based selection process, and the application of fuzzy theory to identify optimal Pareto outcomes. This methodology is not only swift in converging to a solution but also demonstrates a high likelihood of reaching the global optimum. Modeling considerations for smart hydrogen storage systems (SHSSs) incorporate significant constraints, notably those associated with electrolyzers, fuel cells, and storage capacities. The algorithm's effectiveness is validated in scenarios involving parking facilities and multiple uncertain resources, demonstrating a robust reduction in specific cost indicators and adjustments in cost dynamics when DRP is considered. Specifically, the algorithm achieves a substantial 42% decrease in cost variability when DRP is excluded, and a 46.9% reduction in cost variability when DRP is included, despite a minor 5% increase in average cost. These outcomes underscore the proposed system's capability to improve resilience and thermal performance under fluctuating conditions, making it a promising solution for future smart energy systems. Further, the results exhibited the complex interplay between cost optimization and operational adjustments in response to demand-side management. [Display omitted] • A novel approach was integrated with renewable energy and hydrogen storage for resilience enhancement. • Efficiency was optimized under fluctuating grid pricing using a hybrid BA-DEA algorithm. • Cost variability was significantly reduced: 42% without DRP, 46.9% with DRP, with a slight cost increase. • Optimal Pareto solutions were effectively achieved using fuzzy theory and non-dominated sorting. • Resilience and efficiency under power cost uncertainties were enhanced by the proposed system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As Electric Vehicles became more important in the field of energy, the disadvantages of lithium‐ion batteries became apparent. This led to an increased interest in metal‐air batteries, particularly aluminum‐air batteries, as alternatives to lithium‐ion batteries. However, aluminum‐air batteries also have their own deficiencies, with one of the main areas of research being the improvement of anode characteristics. Improving anode characteristics is a crucial area of research in this field. Aluminum alloys have traditionally been used to overcome obstacles in creating batteries with favorable electrochemical properties. However, recent studies suggest that reducing the grain size of aluminum anodes can have positive effects. In this study, we investigated the electrochemical behavior of three aluminum alloys that were grain‐refined by Al‐5Ti‐1B and compared the results to a commonly used aluminum alloy as an anode in aluminum‐air batteries. We chose Al−Ti alloys with variable amounts of titanium (0.01, 0.05, and 0.1 wt.%) for evaluation. We selected a 4 molar potassium hydroxide solution as the test electrolyte due to the merits of alkaline electrolytes. Our study found that the alloy with 0.05 wt.% of titanium is the best anode choice. This anode has promising discharge characteristics at high current density (50 mA/cm2) and is an appropriate choice for electric vehicles. [ABSTRACT FROM AUTHOR]

This study investigates the effectiveness of positive position feedback (PPF) in reducing vibration amplitudes in an electric vehicle generator, specifically at super harmonic resonance (SHR) with 1:1 Internal Resonance (IR). Here is a breakdown. Simplified Model: The study uses a simplified nonlinear dynamic model (one degree of freedom, up to fifth-order nonlinear components) with external force, analyzed using the Multiple Time Scales Method (MTSM) with a first-order approximation. Focus on Resonance: The primary focus is on understanding the system's behavior at SHR with 1:1 IR and how PPF can mitigate vibrations in this specific scenario. Frequency Response and Controller Influence: Frequency response functions are used to analyze the system's stability with PPF, examining how different controller parameters affect the main system's dynamics. Validation: Numerical solutions, obtained using the fourth-order Runge–Kutta method ('RK-4'), are used to demonstrate and evaluate the system's amplitude with and without PPF. The analytical and numerical results show strong agreement, validating the model's accuracy. In essence, the research explores using PPF as a vibration control strategy in a specific resonance condition within an electric vehicle generator, using a combination of analytical and numerical methods for analysis and validation. [ABSTRACT FROM AUTHOR]

Electric vehicle technology is transitioning from mobility based on fossil fuel combustion to one based on vehicle electrification, in which the primary energy is increasingly renewable, and the generation of pollutants and CO2 emissions is being reduced. This paper provides a tour of the key aspects of these systems, reviewing their most important historical, legislative, and grid impact topics. For this purpose, a literature review of publications up to 2022 is conducted. The last decade is the subject of a deeper analysis, shedding light on the essential characteristics of this technology and fundamentally focusing on its integration into electrical distribution networks. This work is carried out based on a review of a selection of articles written by authors worldwide who have researched these topics. We ordered and analyzed the temporal evolution of the defined categories, obtaining their research line direction. A meta-analysis of grid impact was also carried out, prompting clear conclusions about the state of the art and potential future works. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the optimization of electric vehicle (EV) battery management using Q-learning, a powerful reinforcement learning technique. As the demand for electric vehicles continues to grow, there is an increasing need for efficient battery-management strategies to extend battery life, enhance performance, and minimize operating costs. The primary objective of this research is to develop and assess a Q-learning-based approach to address the intricate challenges associated with EV battery management. This paper starts by elucidating the key challenges inherent in EV battery management and discusses the potential advantages of incorporating Q-learning into the optimization process. Leveraging Q-learning's capacity to make dynamic decisions based on past experiences, we introduce a framework that considers state-of-charge, state-of-health, charging infrastructure, and driving patterns as critical state variables. The methodology is detailed, encompassing the selection of state, action, reward, and policy, with the training process informed by real-world data. Our experimental results underscore the efficacy of the Q-learning approach in optimizing battery management. Through the utilization of Q-learning, we achieve substantial enhancements in battery performance, energy efficiency, and overall EV sustainability. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach, demonstrating compelling results. Our Q-learning-based method achieves a significant 15% improvement in energy efficiency compared to conventional methods, translating into substantial savings in operational costs and reduced environmental impact. Moreover, we observe a remarkable 20% increase in battery lifespan, showcasing the effectiveness of our approach in enhancing long-term sustainability and user satisfaction. This paper significantly enriches the body of knowledge on EV battery management by introducing an innovative, data-driven approach. It provides a comprehensive comparative analysis and applies novel methodologies for practical implementation. The implications of this research extend beyond the academic sphere to practical applications, fostering the broader adoption of electric vehicles and contributing to a reduction in environmental impact while enhancing user satisfaction. [ABSTRACT FROM AUTHOR]

The dominant source of the vehicle fleet in developing nations is the used vehicle market in developed nations. As the automotive fleet in developed nations electrifies, so will the used vehicle market. In many cases, developing nations' electric infrastructure is inadequate to support significant vehicle electrification. Therefore, there is an immediate need for developing nations to plan and prepare for vehicle electrification before scarcity of fossil fueled vehicles constitutes a national emergency. This research presents the Developing Nations Electric Vehicle Adoption Model (DN-EVAM), a decision support tool designed to help decision makers in developing nations address this challenge. We then use DN-EVAM to analyze the vehicle adoption landscape of Nigeria. First, we survey subject matter experts in Nigeria to identify antecedent and moderating variables relevant to Nigeria. Then we implement DN-EVAM to model the Nigerian vehicle electrification landscape. Finally, we conduct a scenario-based analysis to identify those antecedent and moderating variables most important to vehicle electrification in Nigeria. We find that for Nigerian policy makers, adoption incentives and infrastructure investments are the most critical areas of focus in the near term while investments in technology development are generally not the most attractive option. [ABSTRACT FROM AUTHOR]

The research of electric vehicle power battery fault diagnosis technology is turning to machine learning methods. However, during operation, the time of occurrence of faults is much smaller than the normal driving time, resulting in too small a proportion of fault data as well as a single fault characteristic in the collected data. This has hindered the research progress in this field. To address this problem, this paper proposes a data enhancement method using Least Squares Generative Adversarial Networks (LSGAN). The method consists of training the original power battery fault dataset using LSGAN models to generate diverse sample data representing various fault states. The augmented dataset is then used to develop a fault diagnosis framework called LSGAN-RF-GWO, which combines a random forest (RF) model with a Gray Wolf Optimization (GWO) model for effective fault diagnosis. The performance of the framework is evaluated on the original and enhanced datasets and compared with other commonly used models such as Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Naïve Bayes (NB). The results show that the proposed fault diagnosis scheme improves the evaluation metrics and accuracy level, proving that the LSGAN-RF-GWO framework can utilize limited data resources to effectively diagnose power battery faults. [ABSTRACT FROM AUTHOR]

This manuscript proposes a hybrid method for drive control using a multilevel inverter (MLI) for electric vehicle (EV) applications. The proposed technique is the joint execution of spiking neural network (SNN), and sand cat swarm optimization (SCSO), hence it is named as SCSO-SNN technique. The primary goal of the proposed technique is to improve the speed tracking performance and, energy efficiency and lessen the torque ripple of the EV. The set of control signals is generated by SCSO and the SNN is used to forecast the optimal control signal of the cascaded multilevel inverter (CMLI). The proposed technique is implemented in the MATLAB software and is evaluated their performance with various present methods. The results show that the proposed method can achieve better performance than current techniques based on speed tracking, energy efficiency, and torque ripple. The proposed method enhanced the total efficiency of the system (96%) and reduced the torque ripple (0.5%) when contrasted with current techniques like Genetic Algorithm (GA), Spotted Hyena Optimizer (SHO), Particle Swarm Optimization (PSO). The proposed method shows a mean of 1.269, a median of 1.212, and a standard deviation of 0.015, indicating high stability and consistency. It also demonstrates lower switching losses (0.159 W) and conduction losses (0.370 W) compared to existing methods, underscoring its superior energy efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Due to insufficient consideration of various factors during the dynamic evaluation of the service capacity of the electric vehicle public charging service network, the evaluation effect is poor. Therefore, the analytic hierarchy process is used to dynamically evaluate the service capacity of the network. This method first conducted an in-depth analysis of the influencing factors of charging service capacity, in order to reasonably arrange the location and quantity of charging stations. Then, considering the travel characteristics of electric vehicle users and their tendency to pursue maximum utility during the charging process, a prediction of the charging demand for charging stations was obtained. Finally, a charging service capability evaluation model was constructed using the analytic hierarchy process. By determining the weights of each evaluation indicator and combining these weights with the actual evaluation scores, the evaluation results were comprehensively calculated. The experimental results show that the proposed method performs well in terms of evaluation accuracy and evaluation duration, with a maximum evaluation accuracy of 98% and a maximum evaluation duration of only 22 s. This indicates that the method can effectively improve the accuracy of evaluation and shorten the evaluation time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Advancements in assisted driving technologies are expected to enable future passengers to use a wide range of multimedia applications in electric vehicles (EVs). To address the bandwidth demands for high-resolution and immersive videos during peak traffic, this study introduces a bandwidth-management algorithm to support differentiated streaming services in heterogeneous vehicle-to-everything (V2X) networks. By leveraging cellular 6G base stations, along with Cell-Free (CF) Massive Multi-Input Multi-Output (mMIMO) Wi-Fi 7 access points, the algorithm aims to provide a high-coverage, high-speed, and low-interference V2X network environment. Additionally, Li-Fi technology is employed to supply extra bandwidth to vehicles with limited connectivity via V2V communication. Importantly, the study addresses the urgency and prioritization of different applications to ensure the smooth execution of emergency applications and introduces a pre-downloading mechanism specifically for non-real-time applications. Through simulations, the algorithm's effectiveness in meeting EV users' bandwidth needs for various multimedia streaming applications is demonstrated. During peak-bandwidth-demand periods, users experienced an average increase in bandwidth of 47%. Furthermore, bandwidth utilization across the V2X landscape is significantly improved. [ABSTRACT FROM AUTHOR]

The electrification of commercial vehicles has led to new wheel torque allocation options for propulsion and braking of articulated heavy vehicles. Each unit or axle can be individually braked or propelled whilst the other units or axles are excluded from such action. This may achieve the best energy efficiency. However, this can also lead to potential yaw stability problems such as jackknifing and trailer swing, especially under bad loading and weather conditions. This paper describes the above instabilities and introduces a nonlinear single-track model to study the vehicle dynamics of the tractor-semitrailer combination. The effects of different vehicle and environment parameters are analysed with this vehicle model. A safe operating envelope for limiting the wheel forces is obtained using this vehicle model. Since the vehicle model introduced in this paper is computationally effective, it can be run online in real vehicles, with an instantaneous safe operating envelope obtained for the momentary conditions. Thus, yaw instabilities such as jackknifing and trailer swing may be avoided. [ABSTRACT FROM AUTHOR]

To solve the shortcomings of the existing hub motors in the practical application of electric vehicles, an integrated dual-rotor hub motor (DRHM) was proposed, which can realize multiple drive modes to adapt to the vehicle's variable driving conditions. Aiming at the complex structure of the DRHM, a multi-objective optimization method of design variables stratification based on comprehensive sensitivity was proposed. The design variables with medium and high sensitivity were optimized by the response surface method and genetic algorithm, respectively. After overall weighing the optimization objectives of output torque, torque ripple, usage amount of permanent magnets and magnetic coupling coefficient, three candidate design were screened out. By the comprehensive performance evaluation of the motor, the optimal structural sizes were determined. Based on a two-dimensional model, the electromagnetic performances of the DRHM were analyzed. The simulation results show that the motor has a small cogging torque and low magnetic coupling degree, and the independent control and stable operation of the internal and external motors can be realized. Besides, the basic characteristics of the DRHM prototype were tested. The experimental results accords well with the simulation results, which show that the proposed motor structure is reasonable and the multi-objective optimization method is effective. [ABSTRACT FROM AUTHOR]

This study aims to determine the mechanical response of a brushless DC motor (BLDC) used in two-wheeled electric vehicles by analyzing torque values through finite element analysis. The motor features a three-phase stator structure and four permanent magnets on its rotor. The study investigates the relationship between torque, pulse degree, excitation voltage, and stator current using RMxprt software. A parametric dataset consisting of 600 data points is generated to model the BLDC motor system. Genetic programming (GP) is employed to establish a formula that correlates the motor's output torque with the input variables. The resulting simplified formula, created with GP, achieves a mean absolute percentage error (MAPE) of 0.085 and an R-squared ( R 2 ) value of 0.989, indicating high accuracy in torque prediction based on simulation parameters. This research provides a torque formulation based on parametric finite element analysis, offering potential benefits for electric bicycles and potentially eliminating the need for certain sensors. Thus, before experimental studies in the process of determining BLDC motor torque behavior, a dataset approach based on FEA parametric simulation studies and a GP formulation developed based on the dataset obtained from parametric simulations were proposed. [ABSTRACT FROM AUTHOR]

This work investigates the design and implementation of a sliding mode controller for a non-isolated dual-input step-up DC–DC converter for electric vehicle applications. The non-isolated dual-input step-up DC–DC converter comprises one inductor, one capacitor, two switches with anti-parallel diodes, and two switches without diodes. The converter analysis is carried out using state space averaged model. The converter with the sliding mode controller is analysed by varying both the input voltage and the load. The performance characteristics of the dual-input DC–DC converter are compared against the conventional PID controller, and it is found that the non-isolated dual-input step-up DC–DC converter with sliding mode controller shows improved performance. The converter thus considered is dynamic and exhibits a high-speed step-up conversion ratio. The sliding mode controller is designed, analysed and it is simulated. The simulation results are validated by developing a prototype model. The converter is simple in construction, highly reliable, and efficient. Further, the converter exhibits less input current ripple and low electromagnetic interference which makes it the best choice for electric vehicle applications. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive analysis of the mechanisms through which air pollution influences consumer adoption of electric vehicles (EVs) in China. Utilizing the Air Quality Index (AQI) and EV sales data from 50 Chinese cities from 2010 to 2019, we apply a multiple regression model to investigate the direct and indirect impacts of air pollution on EV adoption. By analyzing data from the AQI and EV sales across 50 Chinese cities between 2010 and 2019, we apply a multiple regression model to dissect the direct and indirect effects of air pollution on the adoption of EVs. We find that the health risks associated with air pollution, such as respiratory and cardiovascular diseases, significantly sway consumer behavior towards EVs. Concerns over health and the financial burden of air pollution-related illnesses drive consumers, especially in wealthier regions, to choose EVs for healthier transportation. Additionally, we note an indirect mechanism: increased public and government awareness of environmental issues. This heightened awareness, coupled with policies promoting EV adoption, indirectly bolsters consumer interest in EVs, acknowledging their role in enhancing air quality. These findings offer vital insights for formulating sustainable transportation policies and urban planning strategies, especially in contexts of severe air pollution. • Air pollution drives EV adoption, particularly for O 3 , PM 10 , and PM 2.5. • This impact is amplified in wealthier regions. • Public awareness and government concern play crucial roles in this influence. • BEV sales are primarily promoted by air pollution. • Regional disparities show significant impact in North China and the Southeast Coastal region. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

This article evaluates the growing prominence of electric vehicles (EVs) driven by factors like cost reduction and increased environmental awareness. It scrutinizes EV progress, focusing on battery technology advancements, charging methods, and emerging research prospects. It also delves into the global EV market status and its future potential. With batteries being a pivotal EV component, this article offers an extensive overview of various battery technologies, spanning from traditional Lead‐acid to modern lithium‐ion batteries. Furthermore, it explores diverse EV charging standards, emphasizing battery energy management, and underscores unexplored research opportunities for both industry and academia. [ABSTRACT FROM AUTHOR]