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Aiming at the demand for subjective evaluation of driveability for fuel cell vehicles, the modeling and evaluation method of driveability for fuel cell vehicles were studied in this paper. Firstly, a real-time model of the fuel cell vehicle powertrain system was established, which included the fuel cell model, power battery model, DC/DC converter model and drive motor model. Secondly, it was integrated with the vehicle dynamics model to form a virtual prototype of a fuel cell vehicle. And a virtual subjective evaluation platform for fuel cell vehicles was built by combining the virtual prototype and high-fidelity driving simulator. Thirdly, a subjective evaluation method of driveability for fuel cell vehicles was proposed, which included the starting performance, acceleration performance, uniform speed performance and tip-in/tip-out performance. Finally, the paper used the platform and method mentioned above to conduct subjective evaluations of the fuel cell vehicles. [ABSTRACT FROM AUTHOR]

This paper presents a bibliometric analysis of battery state estimation in electric vehicles. In this paper, a quick study is performed on the top global research contributors, funding agencies, and affiliate universities or institutes performing research on this topic while also finding the top keyword searches and top authors based on the most citations in the field of electric vehicles. Trend analysis is done by using the SCOPUS and Web of Science (WOS) databases (DB) from the period of 2000 to 2021. Battery state estimation plays a major role in the battery present state based on past experience. Battery available charge and health knowledge is a must for range estimation and helps us acknowledge if a battery is in useful condition or needs maintenance or replacement. A total of 136 documents in SCOPUS and 1311 documents in Web of Science were analyzed. Through this bibliometric analysis, we learn the top authors, country, publication journal, citation, funding agency, leading documents, research gap, and future trends in this research direction. The author Xiong Rui has the most publications, and he is working at the Beijing Institute of Technology, China. The most common institution is the Beijing Institute of Technology, and China is the most highly contributing country in this research. Most of the publications are conference types in SCOPUS DB and article types in WOS DB. The National Natural Science Foundation of China provides the most funding. The journal Energies has the most publications related to this field. The most cited works are by the authors M.A. Hannan and L.G. Lu in SCOPUS and WOS DB, respectively. A statistical analysis of the top ten countries' productivity results is also discussed. [ABSTRACT FROM AUTHOR]

Aiming at the traditional CUK equalizer can only perform energy equalization between adjacent batteries, if the two single batteries that need to be equalized are far away from each other, there will be the problem of longer energy transmission path and lower equalization efficiency, this paper optimizes the CUK equalizer and optimizes its peripheral selection circuit, which can support the equalization of single batteries at any two positions. The control strategy adopts the open-circuit voltage (OVC) of the battery and the state of charge (SOC) of the battery as the equalization variables, and selects the corresponding equalization variables according to the energy conditions of the two batteries that need to be equalized, and generates the adaptive equalization current with an adaptive PID controller in order to improve the equalization efficiency. Simulation modeling is performed in Matlab/Simulink 2021b, and the experimental results show that the optimized CUK equalizer in this paper improves the equalization time by 25.58% compared with the traditional CUK equalizer. In addition, compared with the mean value difference (MVD) method, the adaptive PID method reduces the equalization time by about 30% in the static and charging and discharging experimental environments, which verifies the superiority of this equalization scheme. [ABSTRACT FROM AUTHOR]

Featured Application: The LM-IEKF algorithm proposed in this paper can effectively estimate the state of charge of a lithium-ion battery, and it is suitable for the estimation of an electric vehicle. The error covariance matrix in the IKEF process is modified by the LM algorithm, and it can still maintain a good convergence speed and estimation accuracy in the face of severe current changes. With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Marquardt (LM) method is used to optimize the error covariance matrix of IKEF. Based on the hybrid pulse power characteristics experiment, a second-order Thevenin model with variable parameters is established on the MATLAB platform. The experimental results show that the proposed model is effective under the constant current discharge condition, the Federal Urban Driving Schedule (FUDS) condition, and the Beijing dynamic stress test (BJDST) condition. The results show that the simulation error of the improved LM-IEKF algorithm is less than 2% under different working conditions, which is lower than that of the IKEF algorithm. The improved algorithm has a fast convergence speed to the true value, and it has a good estimation accuracy in the case of large changes in external input current. Additionally, the fluctuation of error is relatively stable, which proves the reliability of the algorithm. [ABSTRACT FROM AUTHOR]

Batteries have been integral components in modern vehicles, initially powering starter motors and ensuring stable electrical conditions in various vehicle systems and later in energy sources of drive electric motors. Over time, their significance has grown exponentially with the advent of features such as "Start & Stop" systems, micro hybridization, and kinetic energy regeneration. This trend culminated in the emergence of hybrid and electric vehicles, where batteries are the energy source of the electric traction motors. The evolution of storage for vehicles has been driven by the need for larger autonomy, a higher number of cycles, lower self-discharge rates, enhanced performance in extreme temperatures, and greater electrical power extraction capacity. As these technologies have advanced, so have they the methods for their disposal, recovery, and recycling. However, one critical aspect often overlooked is the potential for battery reuse once they reach the end of their useful life. For each battery technology, specific regeneration methods have been developed, aiming to restore the battery to its initial performance state or something very close to it. This focus on regeneration holds significant economic implications, particularly for vehicles where batteries represent a substantial share of the overall cost, such as hybrid and electric vehicles. This paper conducts a comprehensive review of battery technologies employed in vehicles from their inception to the present day. Special attention is given to identifying common failures within these technologies. Additionally, the scientific literature and existing patents addressing regeneration methods are explored, shedding light on the promising avenues for extending the life and performance of automotive batteries. [ABSTRACT FROM AUTHOR]

In the COVID-19 era, automobiles with internal combustion engines are being replaced by eco-friendly vehicles. The demand for battery electric vehicles (BEVs) has increased explosively. Treatment of spent batteries has received much attention. Battery life can be extended via both efficient charging and driving. Consideration of the vehicles ahead when driving a BEV effectively prolongs battery life. Several studies have presented eco-friendly driving profiles for BEVs, the cited authors did not develop a BEV driving profile that considered battery life using reinforcement learning. Here, this paper presents a method of driving profile optimization that increases BEV battery life. This paper does not address how to regenerate spent batteries in an eco-friendly manner. The BEV driving profile is optimized employing a deep Q-network (a reinforcement learning method). This paper uses simulations to evaluate the effect of the driving profile on BEV battery life; these verified the applicability of our model. Finally, the speed profile optimization method was limited to improve energy efficiency or battery life in rapid speed change sections. [ABSTRACT FROM AUTHOR]

The effect of the morphology of Lyocell fibrillated fibers with different beating revolutions on pore structure and electrochemical properties of Lyocell fibrillated fiber paper-based (LF) separators for lithium-ion batteries is investigated. With raising the beating revolutions from 50,000 to 600,000, the number of coarse fibers decreases from 7851 to 743, and the mean fiber diameter decreases from 374 nm to 171 nm. Both the number of coarse fibers and the mean fiber diameter have good correlation with the thickness, pore size and porosity of the LF separators. The risk of battery short circuit is increased, if the mean pore size of separators is larger than the minimum size of cathode particles. Compared to the PE and PI separators, the LF separators deliver excellent cycle performance with different current densities at 30 °C owing to their larger pore size, porosity, and better electrolyte affinity. However, the cycle performance deteriorates at −10 °C due to the severe local lithium plating on the graphite surface caused by a large number of coarse fibers with a diameter ranging from 5 μm to 15 μm as local microscopic defects. • Good correlation between fiber morphology and pore structure. • Increasing risk of short circuit due to mean pore size larger than minimum size of cathode particles. • LF separators deliver excellent cycle performance at 30 °C due to larger pore size, porosity and good electrolyte affinity. • Severe lithium plating at −10 °C caused by coarse fibers as local microscopic defects. [ABSTRACT FROM AUTHOR]

Durability is one of the technical bottlenecks restricting fuel cell electric vehicle development. As a result, significant time and resources have been invested in research related to this area worldwide. Current durability research mainly focuses on the single cell and stack levels, which is quite different from the usage scenarios of actual vehicles. There is almost no research on developing durability test cycles on the fuel cell system level. This paper proposes a universal model for developing a durability test cycle for fuel cell system based on the China automotive test cycle. Large-scale comparison tests of the fuel cell systems are conducted. After 1000 h test, the output performance degradation of three mass-produced fuel cell system is 14.49%, 9.59%, and 4.21%, respectively. The test results show that the durability test cycle proposed in this paper can effectively accelerate the durability test of the fuel cell system and evaluate the durability performance of the fuel cell system. Moreover, the methodology proposed in this paper could be used in any other test cycles such as NEDC (New European Driving Cycle), WLTC (Worldwide Harmonized Light Vehicles Test Procedure), etc. And it has comprehensive application value and are significant for reducing the cost of durability testing of fuel cell systems and promoting the industrialization of fuel cell electric vehicles. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used in modern society as important energy storage devices due to their high energy density, rechargeable performance, and light weight. However, the capacity and performance of lithium-ion batteries gradually degrade with the number of charge or discharge cycles and environmental conditions, which can affect the reliability and lifetime of the batteries, so it is necessary to accurately evaluate their health. The belief rule base (BRB) model is an evaluation model constructed based on rules that can handle uncertainties in the operation of lithium-ion batteries. However, lithium-ion batteries may be affected by disturbances from internal or external sources during operation, which may affect the evaluation results. To prevent this problem, this paper proposes a disturbance-considering BRB modeling approach that considers the possible effects of disturbances on the battery in the operating environment and quantifies the disturbance-considering capability of the assessment model in combination with expert knowledge. Second, robustness and interpretability constraints are added in this paper, and an improved optimization algorithm is constructed that maintains or possibly improves the resistance of the model to disturbance. Finally, using the lithium-ion batteries provided by the National Aeronautics and Space Administration (NASA) Prediction Centre of Excellence and the University of Maryland as a case study, this paper verifies that the proposed modeling approach is capable of constructing robust models and demonstrates the effectiveness of the improved optimization algorithm. [ABSTRACT FROM AUTHOR]

The papers in this special section were presented at the IEEE Vehicle Power and Propulsion Conference (VPPC) that was held in Coimbra, Portugal, in October 2014. Advanced traction and propulsion systems are developing in order to ensure better energy performance, reduced operating cost, and higher lifetime of future transportation systems like road vehicles, as well as more electric trains, subways, ships, and airplanes. Such a powertrain integrates several complex subsystems (including power or energy sources, electricmachines, power electronics,mechanical transmission), and it becomesmandatory to consider the whole system in order to reach the best performance. [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]

To further improve the braking energy recovery efficiency of battery electric vehicles and increase the range of the cars, this paper proposes a multi-mode switching braking energy recovery control strategy based on fuzzy control. The control strategy is divided into three modes: single-pedal energy recovery, coasting energy recovery, and conventional braking energy recovery. It takes the accelerator pedal and brake pedal opening as the switching conditions. It calculates the front and rear wheel braking ratio allocation coefficients and the motor braking ratio through fuzzy control to recover braking energy. The genetic algorithm (GA) is used to update the optimized affiliation function to optimize the motor braking allocation ratio through fuzzy control, and joint simulation is carried out based on the NEDC (New European Driving Cycle) and CLTC-P (China Light-duty Vehicle Test Cycle for Passenger vehicles) cycle conditions. The results show that the multi-mode braking energy recovery control strategy proposed in this paper improves the energy recovery rate and range contribution rate by 4% and 9.6%, respectively, and increases the range by 22.5 km under NEDC cycle conditions. It also improves the energy recovery rate and range contribution rate by 8.7% and 5.5%, respectively, and increases the range by 13 km under CLTC-P cycle conditions, which can effectively improve the energy recovery efficiency of the vehicle and increase the range of battery electric vehicles. [ABSTRACT FROM AUTHOR]

This paper focuses on the analysis and control strategy of a four-switch boost-buck AC/DC converter utilized in power factor correction applications with a wide output voltage range. Given the increasing importance of electric vehicles and the need for high reliability, this study addresses the internal dynamic stability problem that can arise in the converter system. The analysis begins with a thorough examination of the system's min-phase characteristic. Despite this, internal dynamic stability issues persist, requiring a solution to ensure a higher power factor and reliability. To address this challenge, this paper proposes the utilization of a damping RC circuit instead of reducing the loop gain bandwidth. To demonstrate the internal dynamic behavior of the converter, small-signal modeling is employed. This modeling highlights the importance of mitigating internal dynamic instability to achieve the desired power factor and reliability. This study emphasizes the significance of proper analysis and control strategies for boost-buck AC/DC converters in power factor correction applications. By addressing internal dynamic stability using a damping RC circuit, the converter can achieve a higher power factor and enhanced reliability, ultimately contributing to the development of more efficient and dependable EV systems. Finally, the feasibility of the proposed analysis and control strategy is confirmed through comprehensive simulations. The simulation results validate the effectiveness of using a damping RC circuit to address the internal dynamic stability problem, leading to an improved power factor and enhanced reliability. [ABSTRACT FROM AUTHOR]

Pulsating Heat Pipe (PHP) is an emerging efficient heat transfer device, that transfers heat passively through oscillating motions of liquid slugs and vapor plugs within the device. PHP is of high effective thermal conductivity with great potential in heat transfer management for various applications. The objective of this review paper is to summarize and analyse the applications of PHP in various fields that have been reported in the open literature, emphasizing on studies reported in past half decade. The thermo-hydraulic behaviour of PHP is influenced by numerous geometric and operational parameters, which are discussed in detail in the first part of the paper. The thermal performance of the PHP under rotation condition, which is seldom discussed in previous review articles, is also discussed. These parameters act individually and in tandem to alter the performance of PHP, which makes its prediction extremely difficult. However, the benefit of numerous influencing parameters is that they can be altered to make PHP suitable for various applications. These highly variable configurations make PHP suitable for applications such as the transfer of absorbed solar energy to the location of interest, waste heat recovery, thermal management of electric vehicle batteries, fuel cells, cooling of electronic components etc. PHPs are recently being studied for applications in cryogenics and cooling of cutting tools in the machining process also. In addition, novel applications of PHP such as high-performance fins for heat exchangers, cooling of building roofs etc. are also being reported. All these applications of PHP reported in the open literature are reviewed and summarized in the present article. [ABSTRACT FROM AUTHOR]

This paper presents a groundbreaking control strategy for a bidirectional battery charger that allows power to be injected into the smart grid while simultaneously compensating for the grid's reactive power using an electric vehicle battery. An artificial neural network (ANN) controller is utilized for precise design to ensure optimal performance with minimal error. The ANN technique is applied to generate sinusoidal pulse width modulation (SPWM) for a bidirectional AC–DC inverter, with the entire algorithm simulated in MATLAB Simulink.The core innovation of this study is the creation of the ANN algorithm, which supports grid compensation using electric vehicle batteries, an approach termed "vehicle-for-grid". Additionally, the paper details the PCB circuit design of the system controlled by the DSP F28379D board, which was tested on a three-phase motor. The total harmonic distortion (THD) of the proposed ANN algorithm is approximately 1.85 % , compared to the MPC algorithm's THD of about 2.85 % . This indicates that the proposed algorithm is more effective in terms of the quality of the power injected into the grid. Furthermore, it demonstrates effective grid compensation, with the reactive power effectively neutralized to 0 KVAR in the vehicle-for-grid mode. [ABSTRACT FROM AUTHOR]

Batteries play a crucial role in the domain of energy storage systems and electric vehicles by enabling energy resilience, promoting renewable integration, and driving the advancement of eco-friendly mobility. However, the degradation of batteries over time remains a significant challenge. This paper presents a comprehensive review aimed at investigating the intricate phenomenon of battery degradation within the realm of sustainable energy storage systems and electric vehicles (EVs). This review consolidates current knowledge on the diverse array of factors influencing battery degradation mechanisms, encompassing thermal stresses, cycling patterns, chemical reactions, and environmental conditions. The key degradation factors of lithium-ion batteries such as electrolyte breakdown, cycling, temperature, calendar aging, and depth of discharge are thoroughly discussed. Along with the key degradation factor, the impacts of these factors on lithium-ion batteries including capacity fade, reduction in energy density, increase in internal resistance, and reduction in overall efficiency have also been highlighted throughout the paper. Additionally, the data-driven approaches of battery degradation estimation have taken into consideration. Furthermore, this paper delves into the multifaceted impacts of battery degradation on the performance, longevity, and overall sustainability of energy storage systems and EVs. Finally, the main drawbacks, issues and challenges related to the lifespan of batteries are addressed. Recommendations, best practices, and future directions are also provided to overcome the battery degradation issues towards sustainable energy storage system. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries, as the main energy storage component of electric vehicles (EVs), play a crucial role in ensuring the safe and reliable operation of the battery systems through monitoring their state of health (SOH). However, temperature variations and battery aging have significant impacts on the internal parameters of lithium-ion batteries, and these changes directly correlate with the accuracy of battery SOH estimation. To address these issues, this paper proposes an estimation method for lithium-ion battery SOH that considers the impact of temperature. The method begins with reconstructing a second-order hybrid equivalent circuit model for lithium-ion batteries, through which an adaptive update rate for battery model parameters is designed. On this basis, a nonlinear observer for battery states is introduced by integrating filters to estimate SOH. The proposed method considers the impact of capacity in the design of the parameter adaptive update rate, enabling the capacity to be dynamically adjusted based on the actual state of the batteries. This reduces the cumulative error in the SOC observer and improves the modeling accuracy of battery models. Experimental results demonstrate that the method proposed in this paper exhibits exceptional performance in SOH estimation under different temperature conditions. The mean absolute error for SOH estimation does not exceed 0.5%, and the root mean square error does not exceed 0.2%. This method can significantly improve the estimation accuracy of SOH, providing a more efficient and accurate solution for battery management systems in EVs. [ABSTRACT FROM AUTHOR]

Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems. Nevertheless, during the actual operation of electric vehicles, battery performance is subject to the influence of the vehicle's operational state and battery characteristic parameters, introducing challenges to safety alerts. In order to address these challenges and achieve precise battery voltage prediction, this paper comprehensively considers the battery characteristics and driving behavior of electric vehicles in both charging and operational states. Mathematical processing, including averaging and variance calculation, is applied to the battery characteristic parameter data and driving behavior data. By integrating historical voltage data and employing a modified gradient boosting decision tree algorithm (GBDT), a fast and accurate online voltage prediction method is proposed. Hyperparameter optimization is employed to minimize prediction voltage errors. The accuracy and timeliness of the predictions are validated through a comprehensive evaluation and comparison of the forecasted voltages. To diagnose anomalies in battery voltage, the paper proposes a fault diagnosis method that combines the Isolation Forest and Boxplot techniques. Finally, utilizing authentic electric vehicle data for validation, the research underscores the capability of the proposed method to achieve accurate voltage predictions six minutes in advance and provide effective fault diagnosis. This investigation carries substantial practical implications for fortifying battery management and optimizing the performance of electric vehicles. [ABSTRACT FROM AUTHOR]

This paper presents operation and control systems for a new modular on-board charger (OBC) based on a SEPIC converter (MSOBC) for electric vehicle (EV) applications. The MSOBC aims to modularise the battery units in the energy storage system of the EV to provide better safety and improved operation. This is mainly achieved by reducing the voltage of the battery packs without sacrificing the performance required by the HV system. The proposed MSOBC is an integrated OBC which can operate the EV during traction and braking, as well as charge the battery units. The MSOBC is composed of several submodules consisting of a full-bridge voltage source converter connected on the ac side and SEPIC converter installed on the battery side. The SEPIC converter controls the battery segments with a continuous current because it has an input inductor which can smooth the battery's currents without the need for large electrolytic capacitors. The isolated version of the SEPIC converter is employed to enhance the system's safety by providing galvanic isolation between the batteries and the ac output side. This paper presents the necessary control loops to ensure the optimal operation of the EV with the MSOBC in terms of charge and temperature balance without disturbing the required modes of operation. The mathematical analyses in this paper are validated using a full-scale EV controlled by TMS320F28335 DSP. [ABSTRACT FROM AUTHOR]

Efficiency of the battery pack largely depends on the resistive losses and heat generation between the interconnections of the battery cells. Grouping of battery cells usually is done in different ways in industries. However, losses vary depending on applications or states of electric vehicle (EV). Therefore, it is necessary to determine the efficiency and heat generation in battery cells as well as battery packs. In practical situations, some battery cells are charged rapidly in comparison to other battery cells. On the other hand, when an EV is in running condition some battery cells are discharged rapidly. As a results battery pack cannot provide better efficiency and its life span is reduced. As an alternative option the inter-cell connection of battery package is needed to reconfigure in an optimized way. In this paper firstly, a battery pack with switches is modeled and then efficiency and temperature variation with respect to time are determined. Then, an experimental setup is investigated to measure the efficiency and temperature rise with respect to time. Results, explained in the paper, demonstrate that battery pack with switches increases the efficiency if it is measured after switching (97–98 %), while temperature increases from 25 °C to 50 °C for different C-rates. [ABSTRACT FROM AUTHOR]

The population growing faster than before, and availability of transportation options is increasing. Automobiles require combustion engines, which require fuel obtained from underground storage. This underground fuel storage is limited and depleting day-by-day. Many nations have set deadlines up to 2040 to stop producing automobiles that run on underground fuels. Researchers have concentrated on alternative modes of fuel for transportation. The world's largest Sedan marketplaces will transition to all-electric vehicles by 2035, providing a glimpse of greener future other than a significant financial prospect. Not only Sedan, the entire world is focussing on only green electric vehicles to maintain sustainability. However, electric vehicle charging stations are operated by using many conventional resources. Therefore, this paper aims to show how self-charging electric vehicles can help to reduce emissions caused by the direct use of conventional resources in charging stations along with the up-to-date status quo of the EV market. The key descriptions of electric vehicles on top of the battery's type which is randomly used in EVs, how the batteries are proficient in preserving and supplying power continuity itself in vehicles are talked about. Finally, the paper is consulting about charging-discharging system of electric vehicles to make the environment cleaner. [ABSTRACT FROM AUTHOR]

With a global urgency to decrease greenhouse gas emissions, there has been an increasing demand for electric vehicles on the roads to replace vehicles that use internal combustion. Subsequently, the demand and consumption of raw materials have increased, and thus, there has been an increasing number of retired lithium-ion batteries (LIBs) that contain valuable elements. This literature review paper looks at the following: lifecycle assessments (LCA) of EV batteries, the recycling of LIBs while analyzing what studies have been conducted to improve recycling processes, what recycling facilities have been established or are being planned, studies on the circular economy, the environmental benefits of recycling end-of-life (EOL) batteries, and how LIB recycling is aligned with the Sustainable Devel opment Goals, focusing in particular on Goal 13: Climate Action. [ABSTRACT FROM AUTHOR]

Lithium–ion batteries are key elements of electric vehicles and energy storage systems, and their accurate State of Charge (SOC) estimation is momentous for battery energy management, safe operation, and extended service life. In this paper, the Multi-Kernel Relevance Vector Machine (MKRVM) and Whale Optimization Algorithm (WOA) are used to estimate the SOC of lithium–ion batteries under different operating conditions. In order to better learn and estimate the battery SOC, MKRVM is used to establish a model to estimate lithium–ion battery SOC. WOA is used to automatically adjust and optimize weights and kernel parameters of MKRVM to improve estimation accuracy. The proposed model is validated with three lithium–ion batteries under different operating conditions. In contrast to other optimization algorithms, WOA has a better optimization effect and can estimate the SOC more accurately. In contrast to the single kernel function, the proposed multi-kernel function greatly improves the precision of the SOC estimation model. In contrast to the traditional method, the WOA-MKRVM has a higher precision of SOC estimation. [ABSTRACT FROM AUTHOR]

With the massive use of lithium-ion batteries in electric vehicles and energy storage, the environmental and resource problems faced by used lithium-ion batteries are becoming more and more prominent. In order to better resource utilization and environmental protection, this paper employs bibliometric and data analysis methods to explore publications related to waste lithium-ion battery recycling from 1984 to 2021. The Web of Science core set from the SCIE online database was used for this article. These findings demonstrate a considerable increase trend in the number of publications published in the subject of recycling used lithium-ion batteries, with a natural-sciences-centric focus. Argonne National Lab, Chinese Academy of Sciences, and China Academic and Scientific Research Center are the top three institutions in terms of quantity of papers published. The affiliated journals corresponding to these three institutions also have high impact factors, which are 106.47, 44.85, and 58.69, respectively. In comparison to comparable institutes in other nations, the American Argonne National Laboratory supports 223 research articles in this area. China and the US make up the majority of the research's funding. The two key aspects of current lithium-ion battery recycling research are material structure research and environmentally friendly recycling. Nevertheless, high-capacity lithium-ion batteries, waste lithium-ion integrated structures, and gentle recycling of spent lithium-ion batteries will be the major aspects of study in the future. It is hoped that the above analysis can bring new ideas and methods to the field of waste lithium-ion battery recycling and provide a basis for the subsequent research and application of waste lithium-ion battery recycling. [ABSTRACT FROM AUTHOR]

The quest for sustainable economic development alternatives is revolutionising the automobile industry toward a more economic and sustainable products. This paper examines the relationship between green (electric) vehicle products and stock market performance. The prior work inclination is on the innovation and diffusion theories, which provides a lens to examine green innovation in auto industry. The paper adopts a fusion of review and quantitative approach. Data were from the International Energy Agency and Investing databases. A fixed effect panel analysis examine how the stock market reacts to two main types of electric vehicles - the Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV). The result adds to new knowledge namely, the BEV product relates to the stock market significantly at P=0.00002 but negatively. However, the PHEV has a positive and significant relationship with the stock market at a P=0.00001. The results offer practical implications including inter alia, manufacturers' introduction of more PHEV products into the consumer market has a high propensity to improve the stock market performance; this provides a useful information for stock market analysis and auto industry strategic planning. It also offers a good innovation case material for business schools. This paper contributes the first modelling of two main types of green car on stock market performance. [ABSTRACT FROM AUTHOR]

Resonant inductive‐based wireless power transfer (WPT) for battery charging has potential applications in electric vehicles (EVs). The EV battery charging process requires the regulation of both charging voltage and current. Duty ratio or frequency control is generally preferred to manage the power flow between the transmitter and receiver coils in the WPT system. In the case of WPT charging, misalignment between the coils and parameter variations are unavoidable issues that result in changes to the output power. Therefore, it is essential to control the power flow to maintain constant current (CC) and constant voltage (CV) modes during battery charging. To address these challenges, various primary‐side control techniques, such as asymmetric clamped mode (ACM), asymmetric duty cycle (ADC), and phase‐shift (PS) fixed frequency control strategies, have been proposed for WPT systems. This paper conducts a comparative analysis of these control methods, considering their output voltage ranges and their ability to maintain zero‐voltage switching (ZVS) for the entire control range. Furthermore, the paper presents a generalized design for reduced‐order small signal modelling, utilizing an extended describing function. The designed controller, based on small signal modelling, will undergo real‐time testing to evaluate its dynamic performance in the series‐series resonant converter. [ABSTRACT FROM AUTHOR]

With the increasing focus on decarbonisation of the transport sector, it is imperative to consider routes to electrify vehicles beyond those achievable using lithium-ion battery technology. These include heavy goods vehicles and aerospace applications that require propulsion systems that can provide gravimetric energy densities, which are more likely to be delivered by fuel cell systems. While the discussion of light-duty vehicles is abundant in the literature, heavy goods vehicles are under-represented. This paper presents an overview of the electrochemical degradation of a proton exchange membrane fuel cell integrated into a simulated Class 8 heavy goods range-extender fuel cell hybrid electric vehicle operating in urban driving conditions. Electrochemical degradation data such as polarisation curves, cyclic voltammetry values, linear sweep voltammetry values, and electrochemical impedance spectroscopy values were collected and analysed to understand the expected degradation modes in this application. In this application, the proton exchange membrane fuel cell stack power was designed to remain constant to fulfil the mission requirements, with dynamic and peak power demands managed by lithium-ion batteries, which were incorporated into the hybridised powertrain. A single fuel cell or battery cell can either be operated at maximum or nominal power demand, allowing four operational scenarios: maximum fuel cell maximum battery, maximum fuel cell nominal battery, nominal fuel cell maximum battery, and nominal fuel cell nominal battery. Operating scenarios with maximum fuel cell operating power experienced more severe degradation after endurance testing than nominal operating power. A comparison of electrochemical degradation between these operating scenarios was analysed and discussed. By exploring the degradation effects in proton exchange membrane fuel cells, this paper offers insights that will be useful in improving the long-term performance and durability of proton exchange membrane fuel cells in heavy-duty vehicle applications and the design of hybridised powertrains. [ABSTRACT FROM AUTHOR]

With the increasing use of renewable energy sources and decentralized power systems, certain challenges have emerged in meeting consumers' electrical energy demands. The intermittent nature of renewable energy generation means that it cannot always align with consumers' needs, resulting in periods of excess energy production when it is not required. To bridge this gap between production and consumption, energy storage systems are necessary. This paper defines the work of an isolated microgrid, which consists of renewable sources (wind and PV) for energy production, households with electric vehicles as consumers, and a combined storage system. This storage system is made from batteries, hydrogen storage, and a control system that defines the best use of the storage. Stored energy is utilized through fuel cells to generate electricity for consumption when renewable sources cannot meet the demand. This paper presents the principles of electrolysis and models of individual elements within such a system, as well as the definition and principle of control of the system functionality based on rules and conditions. The proposed control system aims to increase the energy storage lifecycle by deciding when and how to utilize which type of storage and define a self-sufficient microgrid based on renewable sources of production. An economic analysis of the storage part of the system was carried out in which the levelized cost of energy stored and the NPC of the storage systems are calculated. A simulation of the system's operation is conducted using one-hour measurements of wind turbines, solar panels, and household consumption in Serbia. To analyze the system's behavior, a one-week time horizon is considered. [ABSTRACT FROM AUTHOR]

This paper investigates the competition between two charge point operators at the same site for future battery electric long-haul trucks. The charge point operators are located along one of the busiest highways in Sweden. The investigation is carried out using an agent-based model where trucks select charge point operators based on charging prices and the length of any queues, while charge point operators adjust their prices and number of chargers to improve their profitability. The study aims to predict conditions for trucks and charge point operators in a future public fast-charging market. Our findings indicate the potential for a well-functioning future public fast-charging market with small queuing problems, high utilisation, and reasonable prices for public fast charging. Assuming a price for electricity of EUR 0.08/kWh and a minimum profit margin of EUR 0.001/kWh for charge point operators, the findings indicate that the price level outside rush hours will be low, approximately EUR 0.1/kWh. The prices during rush hours will likely be much higher, but it is harder to predict the value due to uncertainties of how charge point operators will act in the future market. Still, from the model result, the price during rush hours is suggested to be just above EUR 0.5/kWh, with an average charging price of around EUR 0.15/kWh. It also seems likely that it is profitable for charge point operators to build enough chargers so that charging queues are short. [ABSTRACT FROM AUTHOR]

This paper studies the diffusion of battery electric vehicle (BEV) technology in Japan. The diffusion process in this study refers to both the innovation and adoption of BEV technology. While previous studies on BEV technological diffusion focus on policy selection, especially domestic policy, little is known about the connectedness between international and domestic factors in the BEV technological diffusion process. This paper aims to enrich the discussion of technology diffusion by expanding the argument that technology diffusion is not only a domestic problem but also an international relations problem using a case study of BEV technology diffusion in Japan. The findings indicate that two international factors push Japan to diffuse BEV technology: Japanese commitment to the global environmental regime and global competition in BEV manufacturing. However, the domestic institution factor hinders the BEV technology diffusion. The production structures of automobile industries and political economy relations between the automobile industry and the government further complicate the diffusion process of BEV technology. [ABSTRACT FROM AUTHOR]

The insufficient generalisation capability of the conventional learning from demonstration (LfD) model necessitates redemonstrations. In addition, retraining the model can overwrite existing knowledge, making it impossible to perform previously acquired skills in new application scenarios. These are not economical and efficient. To address the issues, in this study, a broad learning system (BLS) and probabilistic roadmap (PRM) are integrated with dynamic movement primitive (DMP)-based LfD. Three key innovations are proposed in this paper: (1) segmentation and extended demonstration: a 1D-based topology trajectory segmentation algorithm (1D-SEG) is designed to divide the original demonstration into several segments. Following the segmentation, a Gaussian probabilistic roadmap (G-PRM) is proposed to generate an extended demonstration that retains the geometric features of the original demonstration. (2) DMP modelling and incremental learning updating: BLS-based incremental learning for DMP (Bi-DMP) is performed based on the constructed DMP and extended demonstration. With this incremental learning approach, the DMP is capable of self-updating in response to task demands, preserving previously acquired skills and updating them without training from scratch. (3) Electric vehicle (EV) battery disassembly case study: this study developed a solution suitable for EV battery disassembly and established a decommissioned battery disassembly experimental platform. Unscrewing nuts and battery cell removal are selected to verify the effectiveness of the proposed algorithms based on the battery disassembly experimental platform. In this study, the effectiveness of the algorithms designed in this paper is measured by the success rate and error of the task execution. In the task of unscrewing nuts, the success rate of the classical DMP is 57.14% and the maximum error is 2.760 mm. After the optimisation of 1D-SEG, G-PRM, and Bi-DMP, the success rate of the task is increased to 100% and the maximum error is reduced to 1.477 mm. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) fast charging systems are rapidly evolving to meet the demands of a growing electric mobility landscape. This paper provides a comprehensive overview of various fast charging techniques, advanced infrastructure, control strategies, and emerging challenges and future trends in EV fast charging. It discusses various fast charging techniques, including inductive charging, ultra-fast charging (UFC), DC fast charging (DCFC), Tesla Superchargers, bidirectional charging integration, and battery swapping, analysing their advantages and limitations. Advanced infrastructure for DC fast charging is explored, covering charging standards, connector types, communication protocols, power levels, and charging modes control strategies. Electric vehicle battery chargers are categorized into on-board and off-board systems, with detailed functionalities provided. The status of DC fast charging station DC-DC converters classification is presented, emphasizing their role in optimizing charging efficiency. Control strategies for EV systems are analysed, focusing on effective charging management while ensuring safety and performance. Challenges and future trends in EV fast charging are thoroughly explored, highlighting infrastructure limitations, standardization efforts, battery technology advancements, and energy optimization through smart grid solutions and bidirectional chargers. The paper advocates for global collaboration to establish universal standards and interoperability among charging systems to facilitate widespread EV adoption. Future research areas include faster charging, infrastructure improvements, standardization, and energy optimization. Encouragement is given for advancements in battery technology, wireless charging, battery swapping, and user experience enhancement to further advance the EV fast charging ecosystem. In summary, this paper offers valuable insights into the current state, challenges, and future directions of EV fast charging, providing a comprehensive examination of technological advancements and emerging trends in the field. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used in electric vehicles and renewable energy storage systems due to their superior performance in most aspects. Battery parameter identification, as one of the core technologies to achieve an efficient battery management system (BMS), is the key to predicting and managing the performance of Li-ion batteries. However, due to the complex chemical reactions and thermodynamic processes inside lithium-ion batteries, coupled with the influence of the external environment, accurate identification of lithium-ion battery parameters has become an urgent problem to be solved. In addition, data-driven parameter identification can enable battery models to better understand battery behavior, which is one of the focuses of future research. For this reason, this paper comprehensively reviews the application of data-driven parameter identification methods in different scenarios. Firstly, the research briefly explains the working principle of lithium-ion batteries and the key parameters affecting their performance. Secondly, this paper deeply discusses data-driven methods for parameter identification, which are widely used nowadays, and provides improvement ideas to address the shortcomings of traditional methods. Finally, the paper discusses the challenges faced by parameter identification technology for lithium-ion batteries and envisages future prospects. [ABSTRACT FROM AUTHOR]

This paper describes the design of a control unit for efficient battery charge management in battery electric vehicles (BEVs). The system design aims at controlling the performance of the charging process of dual lithium-ion battery blocks in electric vehicles, with a main battery that powers the vehicle and an auxiliary one for servicing the ancillary equipment. In this paper, we design and analyze the protocol of a control unit that operates and regulates the battery charge in electric vehicles to obtain optimum performance. The so-designed system enhances the battery charge process and protects the main battery from capacity reduction, thus enlarging the driving range of the electric vehicle. We design a specific protocol for an electric circuit that reproduces the structure of the battery charge system of an electric vehicle. The control system improves the efficiency of the auxiliary battery charge by 4.5%. The theoretical simulation matches experimental values in a simulation test by 98.4%. [ABSTRACT FROM AUTHOR]

Public transportation systems, including trams and buses, play a crucial role in urban traffic. However, these traditional modes of transport have some well-known drawbacks, such as long distances between stops, lengthy waiting times, and a lack of privacy. In response to these challenges, an innovative mobility concept called "FLAIT-train" offers potential solutions. The FLAIT-train operates on regular roads and aims to provide DOOR-2-DOOR transport, addressing the issues associated with fixed stops and offering increased accessibility and convenience. In its initial phase, the FLAIT-train operates on exclusive lanes, but it is designed to integrate with other traffic eventually. The vehicle technology of FLAIT-trains closely resembles that of battery electric autonomous vehicles. To assess whether FLAIT-trains can be used as a suitable alternative to conventional public transportation systems, this paper employs traffic simulations that consider key performance indicators, including the average waiting time per passenger, maximum waiting time of a single passenger, average in-vehicle time per passenger, and average occupancy rate of the vehicles. Using SUMO software ("Simulation of Urban Mobility", version 1.12.0), a night bus service scenario is meticulously designed and generated. Within this scenario, both FLAIT-trains and conventional buses are simulated under identical conditions and based on statistical data. [ABSTRACT FROM AUTHOR]

This paper analyzes global sales and trade trends for battery electric vehicles (BEVs) and BEV batteries as well as the U.S. BEV battery supply chain. It finds that global BEV sales and trade in BEVs and BEV batteries have grown significantly since 2018, with China leading in BEV sales, production, and battery exports. U.S. sales of BEVs have grown considerably during this period but continue to trail China and the European Union (EU). Production of U.S. BEVs grew faster than U.S. battery production, leading to increased use of imported battery cells in 2022 and 2023. As of 2023, U.S. BEV battery production relies on imports for cathodes, anodes, and other battery components. This paper also includes evidence that a sharp increase in U.S. investment in BEV, BEV battery, and battery component production--due to increased demand and incentives from the Inflation Reduction Act--will likely lead to U.S. content composing a greater share of content in BEVs produced in the United States. [ABSTRACT FROM AUTHOR]

Electric vehicles with proper battery charging mechanism are essential to achieve superior performance with good dynamic response and high efficiency. This paper comes up with the analysis of interval type-2 fuzzy logic controller (IT2FLC) for interleaved Cuk converter for battery charging in electric vehicles. The key intention of this paper is to obtain excellent controller parameters such as improved accuracy and stability with good power quality attributes of less harmonic distortion and unity power factor at the supply side using a robust and intelligent control approach. IT2FLC is developed effectively to acquire the optimal proportional integral (PI) parameters for the constant current and constant voltage charging controllers to enrich the operation of the battery charging system. This in turn achieves excellent transient parameters with less settling time of 0.01 s, reduced overshoot of 1% and efficiency of about 93.85%. The so-called interval type-2 (IT2) controller is therefore accomplished to alleviate uncertainties and improvise the dynamic stability of the charging solution. The behavioural traits of the intelligent controller are examined and compared with Ziegler–Nicholas tuned PI and T1FL-based PI using MATLAB/Simulink. A hardware prototype of 350 W, 48 V/5 A charger is built and verified with dsPIC33F to evaluate the working principle of the converter using IT2. [ABSTRACT FROM AUTHOR]

This paper examines the extent to which it is possible to replace conventional light commercial vehicles in the heating, ventilation and air conditioning and plumbing trade with battery electric vehicles with an unchanged usage profile. GPS trackers are used to record the position data of 22 craft vehicles with combustion engines from eleven companies over the duration of one working week. Within this paper, various assumptions (battery capacity and average consumption) are made for battery electric vehicles and the charging power on the company premises. The potential of battery electric vehicles is evaluated based on the assumption that they are charged only on company premises. Using the collected data and the assumptions made, theoretical state of charge curves are calculated for the vehicles. The driving profiles of the individual vehicles differ greatly, and the suitability of battery electric vehicles should be considered individually. Battery capacity, vehicle energy consumption and charging power at the company have a substantial influence on the suitability of battery electric vehicles. Furthermore, there are differences between vehicles that can charge on the company premises at night and those that cannot or can only do so on some days. [ABSTRACT FROM AUTHOR]

This paper presents an overview of the status and future prospects of fuel-cell electric vehicles (FC-EVs). As global concerns about emissions escalate, FC-EVs have emerged as a promising substitute for traditional internal combustion engine vehicles. This paper discusses the fundamentals of fuel-cell technology considering the major types of fuel cells that have been researched and delves into the most suitable fuel cells for FC-EV applications, including comparisons with mainstream vehicle technologies. The present state of FC-EVs, ongoing research, and the challenges and opportunities that need to be accounted for are discussed. Furthermore, the comparison between promising proton-exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) technologies used in EVs provides valuable insights into their respective strengths and challenges. By synthesizing these aspects, the paper aims to provide a comprehensive understanding and facilitate decision-making for future advancements in sustainable FC-EV transportation, thereby contributing to the realization of a cleaner, greener, and more environmentally friendly future. [ABSTRACT FROM AUTHOR]

The surge in electric vehicle adoption has resulted in a significant rise in end-of-life batteries, which are unsuitable for demanding EV applications. Repurposing these batteries for secondary applications presents a promising avenue to tackle environmental and economic challenges associated with their disposal. The second-life battery (SLB) approach emerges as a mechanism to manage this massive amount of retired EV batteries. However, this approach poses significant challenges in determining and monitoring battery degradation and performance. After evaluating different scenarios for reusing or recycling retired EV batteries, this paper examines the main challenges associated with SLBs, including techno-economic aspects, uncertainty from first life, safety, characterization and screening, battery-management systems, and secondary applications. A comprehensive review of current state-of-the-art SLB research and implementations is provided, particularly emphasizing battery characterization and the requisite evaluation processes for SLB eligibility. This paper explores diverse measurement techniques for assessing SLB performance, evaluating them based on accuracy, complexity, and time consumption, which are essential for achieving cost-effective SLB applications. The overarching objective is to thoroughly understand the principal challenges associated with repurposing EV batteries and delineate the research imperatives necessary for their successful implementation and prolonged lifespan. [ABSTRACT FROM AUTHOR]

This paper develops a model for lithium-ion batteries under dynamic stress testing (DST) and federal urban driving schedule (FUDS) conditions that incorporates associated hysteresis characteristics of 18650-format lithium iron-phosphate batteries. Additionally, it introduces the adaptive sliding mode observer algorithm (ASMO) to achieve robust and swiftly accurate estimation of the state of charge (SOC) of lithium-iron-phosphate batteries during electric vehicle duty cycles. The established simplified hysteresis model in this paper significantly enhances the fitting accuracy during charging and discharging processes, compensating for voltage deviations induced by hysteresis characteristics. The SOC estimation, even in the face of model parameter changes under complex working conditions during electric vehicle duty cycles, maintains high robustness by capitalizing on the easy convergence and parameter insensitivity of ASMO. Lastly, experiments conducted under different temperatures and FUDS and DST conditions validate that the SOC estimation of lithium-iron-phosphate batteries, based on the adaptive sliding-mode observer and the simplified hysteresis model, exhibits enhanced robustness and faster convergence under complex working conditions and temperature variations during electric vehicle duty cycles. [ABSTRACT FROM AUTHOR]

As a core component of new energy vehicles, accurate estimation of the State of Health (SOH) of lithium-ion power batteries is essential. Correctly predicting battery SOH plays a crucial role in extending the lifespan of new energy vehicles, ensuring their safety, and promoting their sustainable development. Traditional physical or electrochemical models have low accuracy in measuring the SOH of lithium batteries and are not suitable for the complex driving conditions of real-world vehicles. This study utilized the black-box characteristics of deep learning models to explore the intrinsic correlations in the historical cycling data of lithium batteries, thereby eliminating the need to consider the internal chemical reactions of lithium batteries. Through Pearson correlation analysis, this study selects health indicators (HIs) from lithium battery cycling data that significantly impact SOH as input features. In the field of lithium batteries, this paper applies ABC-BiGRU for the first time to SOH prediction. Compared with other recursive neural network models, ABC-BiGRU demonstrates superior predictive performance, with maximum root mean square error and mean absolute error of only 0.016799317 and 0.012626847, respectively. [ABSTRACT FROM AUTHOR]

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor- (SC-) based hybrid energy storage system (HESS) using hybrid proportional and integral- (PI-) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two-loop voltage and current PI controllers with low-pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI-sliding mode controller (SMC) scheme is designed to control the bidirectional DC-DC converters to overcome the drawbacks of aforementioned issues. The combined PI-SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB-Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI-SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI-SMC scheme outperforms in transient and steady-state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set-up is developed using dSPACE-1104, and the experimental results have been carried out to verify the proposed system's feasibility. [ABSTRACT FROM AUTHOR]

The Indonesian government has targeted 2.1 million two-wheeled electric vehicles and 2,200 four-wheeled electric vehicles (EV) by 2025. This is hampered by limited electricity supply and EV charging, which takes long time. Multi device interleaved DC-DC bidirectional converter has been applied and assessed as the most suitable method for battery EV and plug-in hybrid EV because it produces high power >10 kW. For power below 10 kW, it is recommended to use a sinusoidal, Z-Source, and boost amplifier type converter. The smart charging (SC) system will be applied to electric vehicles, which only require a minimum charging power of around 169 W for four lead acid batteries. This paper focuses on an SC system that is capable of charging the battery quickly while still paying attention to the state of health (SoH) of the battery. The SC developed uses a DC-DC boost converter to increase the voltage produced by the switch mode power supply (SMPS). Estimated charging time is less than 30 minutes and still pay attention to the battery SoH. SC will also use pulse width modulation (PWM) as a duty power cycle regulator. This research applies a multi-layer perceptron (MLP) classifier to a neural network (NN). The results of the research show that smart charging can charge up to 600 W with an estimated charging time of around 11 minutes. The charging condition is above 60 % and the power duty cycle setting is 100 %. The power estimation results processed using the ant colony optimization (ACO) based neural network method show a root mean square deviation value of 0.010013430 for charging four lead acid batteries. These results are useful to help solve the problem of capacity requirements and battery charging speed for EVs, with good SoH. [ABSTRACT FROM AUTHOR]

In recent years, there has been rapid advancement in new energy technologies aimed at mitigating greenhouse gas emissions stemming from fossil fuels. Nonetheless, uncertainties persist in both the power output of new energy sources and load. To effectively harness the economic and operational potential of an Integrated Energy System (IES), this paper introduces an enhanced uncertainty set. This set incorporates N-1 contingency considerations and the nuances of source–load distribution. This framework is applied to a robust optimization model for an Electric Vehicle Integrated Energy System (EV-IES), which includes Electric Vehicle Battery Swapping Station (EVBSS). Firstly, this paper establishes an IES model of the EVBSS, and then proceeds to classifies and schedules the large-scale battery groups within these stations. Secondly, this paper proposes an enhanced uncertainty set to account for the operational status of multiple units in the system. It also considers the output characteristics of both new energy sources and loads. Additionally, it takes into consideration the N-1 contingency state and multi-interval distribution characteristics. Subsequently, a multi-time-scale optimal scheduling model is established with the objective of minimizing the total cost of the IES. The day-ahead robust optimization fully considers the multivariate uncertainty of the IES. The solution employs the Nested Column and Constraint Generation (C&CG) algorithm, based on the distribution characteristics of multiple discrete variables in the model. The intraday optimal scheduling reallocates the power of each unit based on the robust optimization results from the day-ahead scheduling. Finally, the simulation results demonstrate that the proposed method effectively reduces the conservatism of the uncertainty set, ensuring economic and stable operation of the EV-IES while meeting the demands of electric vehicle users. [ABSTRACT FROM AUTHOR]

Accurately identifying a specific faulty monomer in a battery pack in the early stages of battery failure is essential to preventing safety accidents and minimizing property damage. While there are existing lithium-ion power battery fault diagnosis methods used in laboratory settings, their effectiveness in real-world vehicle conditions is limited. To address this, fault diagnosis methods for real-vehicle conditions should incorporate fault characteristic parameters based on external battery fault characterization, enabling the accurate identification of different fault types. However, these methods are constrained when confronted with complex fault types. To overcome these limitations, this paper proposes a battery fault diagnosis method that combines multidimensional fault features. By merging different fault feature parameters and mapping them to a high-dimensional space, the method utilizes a local outlier factor (LOF) algorithm to detect anomalous values, enabling fault diagnosis in complex working conditions. This method improves the detection time by an average of 22 min compared to the extended RMSE method and maintains strong robustness while correctly detecting faults compared to other conventional methods. [ABSTRACT FROM AUTHOR]

Global warming, pollution, and increasing energy demand have compelled electrification of the transport sector. Electric vehicles are not only an attractive and cleaner mode of transport, but they also possess the capacity to offer flexible storage alternative based on bidirectional vehicle-to-grid schemes. Vehicle-to-grid or V2G technology permits electric vehicles' batteries to store energy and discharge it back to the power grid during peak-load periods. However, the feasibility and economic viability of V2G is still a matter of concern and needs investigation. In this paper, the authors delved into the feasibility of V2G technology by analysing the real time-charging data of a V2G demonstration project named EV-elocity, located at the University of Nottingham campus in the UK. The authors analysed the charging data and trip-status data of two charging sites and put forward some insights regarding the feasibility of V2G and the behavioural traits of the vehicles. This paper will enlighten the research community regarding the feasibility and benefits of V2G in a real-world environment by analysing the charging/discharging and vehicle behaviour and reporting the opportunities and benefits of vehicle-to-grid technology. [ABSTRACT FROM AUTHOR]

The implementation of energy storage system (ESS) technology with an appropriate control system can enhance the resilience and economic performance of power systems. However, none of the storage options available today can perform at their best in every situation. As a matter of fact, an isolated storage solution's energy and power density, lifespan, cost, and response time are its primary performance constraints. Batteries are the essential energy storage component used in electric mobility, industries, and household applications nowadays. In general, the battery energy storage systems (BESS) currently available on the market are based on a homogeneous type of electrochemical battery. However, a hybrid energy storage system (HESS) based on a mixture of various types of electrochemical batteries can potentially provide a better option for high-performance electric cars, heavy-duty electric vehicles, industries, and residential purposes. A hybrid energy storage system combines two or more electrochemical energy storage systems to provide a more reliable and efficient energy storage solution. At the same time, the integration of multiple energy storage systems in an HESS requires advanced control strategies to ensure optimal performance and longevity of the system. This review paper aims to provide a comprehensive overview of the control systems used in HESSs for a wide range of applications. An overview of the various control strategies used in HESSs is offered, including traditional control methods such as proportional–integral–derivative (PID) control, and advanced control methods such as model predictive control (MPC), droop control (DC), sliding mode control (SMC), rule-based control (RBC), fuzzy logic control (FLC), and artificial neural network (ANN) control are discussed. The paper also highlights the recent developments in HESS control systems, including the use of machine learning techniques such as deep reinforcement learning (DRL) and genetic algorithms (GA). The paper provides not only a description and classification of various control approaches but also a comparison between control strategies from the evaluation of performance point of view. The review concludes by summarizing the key findings and future research directions for HESS control systems, which is directly linked to the research on machine learning and the mix of different control type strategies. [ABSTRACT FROM AUTHOR]