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With the development of sustainable economy, new energy materials are widely used in various industries, and many cars also adopt new energy power batteries as power sources. However, it is currently not possible to accurately diagnose faults in power batteries, which results in the safety of power batteries not being guaranteed. To address this issue, this study utilizes the Whale Optimization Algorithm to improve the Long Short-Term Memory algorithm and constructs a fault diagnosis model based on the improved algorithm. The purpose of using this model for fault diagnosis of power batteries is to strengthen the safety management of batteries. This study first conducted experiments on the improved algorithm and obtained an accuracy of 95.3%. The simulation results of the fault diagnosis model showed that the diagnosis time was only 1.2s. The analysis of the power battery showed that after using this model, the safety performance has been improved by 90.1%, while the maintenance cost has been reduced to 20.3% of the original. The above results verify that the fault diagnosis model based on the improved algorithm can accurately diagnose faults in power batteries, thereby improving the safety of power batteries. [ABSTRACT FROM AUTHOR]

The power battery configuration of an extended-range electric vehicle directly affects the overall performance of the vehicle. Optimization of the output voltage of the power battery can improve the overall power and economy of the vehicle to ensure its safe operation. Factors affecting the output voltage of power batteries under different operating conditions, such as nominal voltage and the number of series and parallel connections of the battery cells, have been studied. This study uses AVL Cruise to establish an overall model of an extended-range electric vehicle to simulate the output voltage characteristics under the different operating conditions of the NEDC (New European Driving Cycle), WLTC (World Light Vehicle Test Cycle) and CLTC (China Light Duty Vehicle Test Cycle). The influence of the output voltage of the power battery under different operating conditions is studied to ensure that the power battery can output energy with high efficiency. The operating conditions have an impact on the output voltage with an idle voltage fluctuation of the operating conditions. The nominal voltage variation and the number of series and parallel connections of the battery cells affect the frequency and time of breakdown. [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]

Abstract With the development of sustainable economy, new energy materials are widely used in various industries, and many cars also adopt new energy power batteries as power sources. However, it is currently not possible to accurately diagnose faults in power batteries, which results in the safety of power batteries not being guaranteed. To address this issue, this study utilizes the Whale Optimization Algorithm to improve the Long Short-Term Memory algorithm and constructs a fault diagnosis model based on the improved algorithm. The purpose of using this model for fault diagnosis of power batteries is to strengthen the safety management of batteries. This study first conducted experiments on the improved algorithm and obtained an accuracy of 95.3%. The simulation results of the fault diagnosis model showed that the diagnosis time was only 1.2s. The analysis of the power battery showed that after using this model, the safety performance has been improved by 90.1%, while the maintenance cost has been reduced to 20.3% of the original. The above results verify that the fault diagnosis model based on the improved algorithm can accurately diagnose faults in power batteries, thereby improving the safety of power batteries.

Purpose – The management issues of this article, and the author is attempting to address these issues, are as follows: What is the optimal decision of each entity in the closed-loop supply chain for the cascading utilization of power batteries under three government measures: no subsidies, subsidies and rewards and punishments? How do different measures affect the process of cascading the utilization of power batteries? Which measures will help incentivize cascading utilization and battery recycling efforts? Design/methodology/approach – The paper uses game analysis methods to study the optimal decisions of various stakeholders in the supply chain under the conditions of subsidies, non-subsidies and reward and punishment policies. The impact of various parameters on the returns of game entities is tested through Matlab numerical simulation. Findings – The analysis discovered that each party in the supply chain will see an increase in earnings if the government boosts trade-in subsidies, which means that the degree of recycling efforts of each entity will also increase; under the condition with subsidies, the recycling efforts and echelon utilization rates of each stakeholder are higher than those under the incentive and punishment measure. In terms of the power battery echelon’s closed-loop supply chain incentive, the subsidy policy exceeds the reward and punishment policy. Originality/value – The article takes the perspective of differential games and considers the dynamic process of exchanging old for new, providing important value for the practice of using old for new behavior in the closed-loop supply chain of power battery cascading utilization.

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]

High-voltage heat release from batteries can cause safety issues for electric vehicles. Relevant scientific research work is carried out in the laboratory. The battery safety of laboratory experiments should not be underestimated. In order to evaluate the safety performance of batteries in the laboratory testing of driving conditions of electric vehicles, this paper simulated and compared the discharge characteristics of two common batteries (lithium iron phosphate (LFP) battery and nickel–cobalt–manganese (NCM) ternary lithium battery) in three different operating conditions. The operating conditions are the NEDC (New European Driving Cycle), WLTP (World Light Vehicle Test Procedure) and CLTC-P (China light vehicle test cycle) for normal driving of electric vehicles. LFP batteries have a higher maximum voltage and lower minimum voltage under the same initial voltage conditions, with a maximum voltage difference variation of 11 V. The maximum current of WLTP is significantly higher than NEDC and CLTC-P operating conditions (>20 A). Low current discharge conditions should be emulated in teaching simulation and experiments for safety reasons. The simulation data showed that the LFP battery had good performance in maintaining the voltage plateau and discharge voltage stability, while the NCM battery had excellent energy density and long-term endurance. [ABSTRACT FROM AUTHOR]

With the aggravation of environmental pollution problems and the reduction of non-renewable energy sources such as oil, new energy vehicles have gradually become the focus of attention, and the application of their power batteries has become more and more widespread. The state of energy (SOE) of the power battery is an important basis for energy scheduling. Therefore, the study used computer technology to develop an analogous model of the power battery and evaluated its properties at various temperatures in order to precisely analyze the performance of the battery under thermal conditions. At the same time, to address the limitations in parameter estimation, the study uses the improved Kalman filter (KF) algorithm to optimize it. The results revealed that the estimation errors of the improved cubature Kalman filter (CKF) algorithm were reduced by 0.52%, 2.91% and 3.10% compared with the traditional CKF algorithm, EKF algorithm and UKF algorithm, respectively. In summary, the research on computer modeling and parameter estimation of the performance of new energy vehicle power batteries under hot working conditions provides important support and reference for the efficient operation and safety of new energy power batteries under hot working conditions.

The power battery in an electric vehicle is an essential part, and the damage to the battery will have a substantial impact on its thermal characteristics. In order to improve the reliability of the air-cooled lithium-ion battery packs in the high temperature environments, this paper offers a more useful and general optimization strategy for the design of the thermal management system for the batteries which have a damaged battery. To ensure the optimal heat dissipation and prolong the battery string operation, the damaged batteries should be swapped out with the backup batteries. The relationship between the target and the design variables which was established using the quadratic polynomial response function is examined. The experimental cases are selected using the optimal Latin hypercube design approach. After optimizing through the multi-island genetic approach, the error between the simulation results and the prediction results is only 0.06 %. The maximum temperature and the maximum temperature difference of the ideal structure are reduced by 4.58 % and 28.05 %, respectively. Then, the backup battery is selected as the sole battery that has the highest temperature of the ideal structure, and each broken battery is replaced by the backup battery when the damaged position is determined. Finally, a parametric equation connecting the temperature and the flow rate is created, and the inlet flow rate is tuned to match the optimal heat dissipation state of the battery pack. The results of the battery thermal management system having a damaged battery in various situations have an important significance for adjusting the operating conditions of the cooling system for the batteries.

Abstract Amidst the industrial transformation and upgrade, the new energy vehicle industry is at a crucial juncture. Power batteries, a vital component of new energy vehicles, are currently at the forefront of industry competition with a focus on technological innovation and performance enhancement. The operational temperature of a battery significantly impacts its efficiency, making the design of a reliable Thermal Management System (TMS) essential to ensure battery safety and stability. Cylindrical power batteries are widely utilized in the industry. This article outlines the four main structures and their drawbacks of TMS for cylindrical power batteries. Among these structures, air cooling falls short in meeting high heat dissipation requirements. Liquid cooling is expensive, intricate, and adds considerable weight. Phase Change Materials (PCM) are not yet prevalent in practical applications. Similarly, heat pipes are relatively uncommon in large high-power battery packs. To better align with the new energy vehicle industry’s demands for top-notch performance, cost-effectiveness, eco-friendliness, and reliability, this paper strongly recommends delving deeper into composite cooling solutions. The construction of an economically viable and fully optimized composite cooling method is poised to become a significant scientific challenge for future research endeavors.

Amidst the industrial transformation and upgrade, the new energy vehicle industry is at a crucial juncture. Power batteries, a vital component of new energy vehicles, are currently at the forefront of industry competition with a focus on technological innovation and performance enhancement. The operational temperature of a battery significantly impacts its efficiency, making the design of a reliable Thermal Management System (TMS) essential to ensure battery safety and stability. Cylindrical power batteries are widely utilized in the industry. This article outlines the four main structures and their drawbacks of TMS for cylindrical power batteries. Among these structures, air cooling falls short in meeting high heat dissipation requirements. Liquid cooling is expensive, intricate, and adds considerable weight. Phase Change Materials (PCM) are not yet prevalent in practical applications. Similarly, heat pipes are relatively uncommon in large high-power battery packs. To better align with the new energy vehicle industry's demands for top-notch performance, cost-effectiveness, eco-friendliness, and reliability, this paper strongly recommends delving deeper into composite cooling solutions. The construction of an economically viable and fully optimized composite cooling method is poised to become a significant scientific challenge for future research endeavors. [ABSTRACT FROM AUTHOR]

Power batteries, the core component of the rapidly evolving electric vehicle industry, have increasingly become a focal point of attention. Recycling power batteries can mitigate environmental pollution and utilize resources efficiently, which is crucial for fostering a low-carbon economy and achieving sustainable development. Utilizing prospect theory, this study proposes a tripartite game model for low-carbon innovation in power battery recycling, involving government agencies, power battery manufacturers, and recycling enterprises. This paper initially identifies the evolutionary stability strategy, subsequently simulates the evolutionary process through parameter assignment, and explores parameter sensitivity along with comparative effects. This study indicates the following: (i) Government incentives are pivotal in motivating manufacturers and recyclers towards low-carbon innovation. (ii) Reducing technology costs and enhancing spillovers significantly boost low-carbon innovation's appeal. (iii) Moderate carbon taxes can encourage businesses to engage in low-carbon innovation, while excessively high taxes may increase operating costs and hinder investment in innovation. Lastly, policy recommendations are made in order to support environmental preservation and the industry's sustainable growth in the power battery recycling sector. [ABSTRACT FROM AUTHOR]

The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy observed in traditional power battery fault diagnosis models, this study proposes a fault diagnosis method utilizing a Convolutional Block Attention Capsule Network (CBAM-CapsNet) based on a stacked sparse autoencoder (SSAE). The reconstructed dataset is initially input into the SSAE model. Layer-by-layer greedy learning using unsupervised learning is employed, combining unsupervised learning methods with parameter updating and local fine-tuning to enhance visualization capabilities. The CBAM is then integrated into the CapsNet, which not only mitigates the effect of noise on the SSAE but also improves the model's ability to characterize power cell features, completing the fault diagnosis process. The experimental comparison results show that the proposed method can diagnose power battery failure modes with an accuracy of 96.86%, and various evaluation indexes are superior to CNN, CapsNet, CBAM-CapsNet, and other neural networks at accurately identifying fault types with higher diagnostic accuracy and robustness. [ABSTRACT FROM AUTHOR]

Abstract The vehicle's power battery is composed of a large number of battery cells series or in parallel. Due to the manufacturing process error and the different use environments, there are differences between the battery cells, and the battery pack will have inconsistency problems, which will increase the safety hazard. Therefore, it is of great practical significance to identify and warn about the inconsistency of power batteries. Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the sliding window (SW) to segment the dataset and update the data of the diagnosis model in real‐time. The scores of normal battery cells and abnormal battery cells were analyzed, and then the fault threshold was determined to be 0.75. The results show that the recall ratio and precision ratio of the algorithm are 0.91 and 0.95, respectively, which is more suitable for inconsistent battery cell identification than other methods. If the SW size is 15, the warning effect is the best. Before the vehicle alarm occurs, the algorithm can realize early fault warnings, thus effectively avoiding the safety problems caused by inconsistency faults.

In the present work, a power battery pack and a novel thermal management system (TMS) based on the tubular heat pipe (THP) technology are designed according to the parameters of a pure electric light truck and battery. The TMS consists of THPs, power batteries, cooling plates, fins, and equivalent thermal conductivity is used to characterize the excellent thermal conductivity of THP. The heat generation rate of the battery at different discharge rates is obtained by establishing a multi-scale electrochemical–thermal coupling model, which accuracy is verified by the experiment. A multi-physics field coupling model is built to the thermal management system designed for the study. The results show that the battery temperature is higher when the battery is far from the THP in the cooling plate, and the maximum temperature reaches 35.6 °C at 1 C. Furthermore, the temperature difference between batteries is proportional to the discharge rate, and the maximum temperature difference reaches 7 °C at 2 C. The change in inlet temperature and wind speed can only restrain the temperature rise, while the temperature difference of the battery module is always 3.2 °C. When a fin spacing of 3 mm is chosen, the best temperature uniformity of the battery module is achieved at this time. The use of the multi-scale multi-physics field coupling analysis method in BTMS with heat pipes can be used as a reference for future numerical simulations of the same type.

With the intensification of energy and environmental crises, new energy vehicles have become a beacon of hope. The performance of their core component, the power battery system, is directly related to the vehicle's range and safety. The heat generation of power batteries during operation is particularly critical, as temperatures that are too high or too low can adversely affect battery performance. Therefore, optimizing the Battery Thermal Management System (BTMS) is of paramount importance. However, existing research has largely focused on system design and experimental validation, while the thermodynamic analysis of battery heat generation mechanisms and the study of performance adaptability under complex conditions remain insufficient. This study aims to delve into the thermodynamic behavior of power batteries by establishing models for their heat generation and dissipation, thereby optimizing the design of the BTMS to enhance the overall performance and safety of the system. Initially, a thermodynamic model of the battery pack under various conditions was constructed. Based on this, existing thermal management technologies were evaluated and optimized, leading to the proposal of viable optimization solutions. The outcomes of this study contribute to improving the energy efficiency and safety levels of new energy vehicle power batteries, holding significant practical and theoretical value for the advancement of the new energy vehicle industry. [ABSTRACT FROM AUTHOR]

New energy vehicles and solid-state batteries (SSBs) will help to reduce the carbon footprint by up to 103% if fully commercialized and installed by 2035. This research collected market data on China's E-car power batteries in the production phase from the past five years to the next 25 years in order to calculate the carbon emission reduction ratio achieved by new electric vehicles' (EVs) power batteries. Using SimaPro software, analysis results reveal that among seven types of batteries, lithium iron phosphate (LFP), cobalt manganese oxide (NCM-811) batteries, and SSBs have the lowest production carbon footprint values of 44, 51.1, and 43.7 kgCO2e , respectively. When compared to LFP and NCM batteries, SSBs have the potential to reduce the carbon footprint of EV batteries by up to 39%. So, SSBs will have a higher market value and installed capacity, accounting for 65% of all batteries by 2040, which can prove the significance of new energy vehicles in reducing carbon emissions in the transportation field. Finally, the five technical and economic characteristics (cost competitiveness, cycle life, C-rate, energy density, and safety) of LFP-based lithium-ion battery (LIB), NMC-811, and lithium lanthanum zirconium oxide (LLZO) based on SSBs batteries are summarized. The promise of the SSBs' energy density and safety has prompted several automakers to invest in NCM-811 and LFP technologies. The cost of SSBs per kWh will eventually be lower than that of its counterparts once supply chains are established. This is because the material cost is reduced when using solid-state electrolytes with higher energy density. After all, less raw material is required per kWh. [ABSTRACT FROM AUTHOR]

The vehicle's power battery is composed of a large number of battery cells series or in parallel. Due to the manufacturing process error and the different use environments, there are differences between the battery cells, and the battery pack will have inconsistency problems, which will increase the safety hazard. Therefore, it is of great practical significance to identify and warn about the inconsistency of power batteries. Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the sliding window (SW) to segment the dataset and update the data of the diagnosis model in real‐time. The scores of normal battery cells and abnormal battery cells were analyzed, and then the fault threshold was determined to be 0.75. The results show that the recall ratio and precision ratio of the algorithm are 0.91 and 0.95, respectively, which is more suitable for inconsistent battery cell identification than other methods. If the SW size is 15, the warning effect is the best. Before the vehicle alarm occurs, the algorithm can realize early fault warnings, thus effectively avoiding the safety problems caused by inconsistency faults. [ABSTRACT FROM AUTHOR]

With the popularity and development of electric vehicles, the demand for power batteries has increased significantly. Power battery recycling requires a complex and efficient logistics network to ensure that used batteries can be safely and cost-effectively transported to recycling centers and properly processed. This paper constructs a dual-objective mathematical model that minimizes the number of recycling centers and minimizes the logistics cost from the service center to the recycling center, and designs the power battery disassembly and recycling process and the recycling logistics network, and finally uses a genetic algorithm to solve it. Finally, this article takes STZF Company as an example to verify the effectiveness of this method. The verification results show that the logistics intensity of the optimized power battery recycling logistics network has been reduced by 36.2%. The method proposed in this article can provide certain reference for power battery recycling logistics network planning.

High-voltage heat release from batteries can cause safety issues for electric vehicles. Relevant scientific research work is carried out in the laboratory. The battery safety of laboratory experiments should not be underestimated. In order to evaluate the safety performance of batteries in the laboratory testing of driving conditions of electric vehicles, this paper simulated and compared the discharge characteristics of two common batteries (lithium iron phosphate (LFP) battery and nickel–cobalt–manganese (NCM) ternary lithium battery) in three different operating conditions. The operating conditions are the NEDC (New European Driving Cycle), WLTP (World Light Vehicle Test Procedure) and CLTC-P (China light vehicle test cycle) for normal driving of electric vehicles. LFP batteries have a higher maximum voltage and lower minimum voltage under the same initial voltage conditions, with a maximum voltage difference variation of 11 V. The maximum current of WLTP is significantly higher than NEDC and CLTC-P operating conditions (>20 A). Low current discharge conditions should be emulated in teaching simulation and experiments for safety reasons. The simulation data showed that the LFP battery had good performance in maintaining the voltage plateau and discharge voltage stability, while the NCM battery had excellent energy density and long-term endurance.

As a kind of clean energy transportation, new energy vehicles are widely respected. This topic focuses on the detection of abnormalities in power batteries in new energy vehicles. After combing the common faults of the battery management system, using the basic structure of RBF neural network and the advantages of the reduced clustering algorithm, for a single power battery, the power battery power abnormality detection scheme based on the improvement of reduced clustering algorithm is proposed, and the power battery abnormality detection process is designed. Taking the sensing feature data of the battery management system of a new energy vehicle as an experimental sample, through the battery state estimation experiment and the example application of the model, it is found that the RMSE (0.0018) and MAPE (0.0206) of the model training are lower than that of the comparison model, and the average error rate of the abnormal battery identification is 0.833%. The model’s abnormality detection results in both instances are consistent with the actual maintenance results. The analysis indicates that the RBF neural network model with reduced clustering algorithm has superior accuracy and feasibility for detecting abnormal battery power.

Reliable quality control of laser welding on power batteries is an important issue due to random interference in the production process. In this paper, a quality inspection framework based on a two-branch network and conventional image processing is proposed to predict welding quality while outputting corresponding parameter information. The two-branch network consists of a segmentation network and a classification network, which alleviates the problem of large training sample size requirements for deep learning by sharing feature representations among two related tasks. Moreover, coordinate attention is introduced into feature learning modules of the network to effectively capture the subtle features of defective welds. Finally, a post-processing method based on the Hough transform is used to extract the information of the segmented weld region. Extensive experiments demonstrate that the proposed model can achieve a significant classification performance on the dataset collected on an actual production line. This study provides a valuable reference for an intelligent quality inspection system in the power battery manufacturing industry. [ABSTRACT FROM AUTHOR]

As the global new energy vehicle (NEV) industry rapidly expands, the disposal and recycling of end-of-life (EOL) power batteries have become imperative. Efficient closed-loop supply chain (CLSC) management, supported by well-designed regulations and strategic investments, plays a crucial role in sustainable waste power battery recycling. In this study, an evolutionary game theory (EGT) methodology is used to construct a tripartite game model to investigate the interactions among manufacturers, recyclers, and the government to study the decision-making dynamics of green investments. In addition, numerical simulations are performed to evaluate the sensitivity of the relevant parameters on the stability of the evolution of the system. The results reveal that government green subsidies can stimulate early period investments in advanced recycling technologies. However, as the battery recycling industry matures, a 'free-rider' behavior emerges among enterprises, which can be mitigated through the imposition of a carbon tax. Eventually, as the industry reaches maturity, manufacturers and recyclers autonomously invest for enhanced profitability. This research provides valuable insights for government policy formulation, facilitating the formal recycling of retired batteries and fostering sustainability in the NEV sector. [ABSTRACT FROM AUTHOR]

In the past decade, in the context of the carbon peaking and carbon neutrality era, the rapid development of new energy vehicles has led to higher requirements for the performance of strike forces such as battery cycle life, energy density, and cost. Lithium-ion batteries have gradually become mainstream in electric vehicle power batteries due to their excellent energy density, rate performance, and cycle life. At present, the most widely used cathode materials for power batteries are lithium iron phosphate (LFP) and LixNiyMnzCo1−y−zO2 cathodes (NCM). However, these materials exhibit bottlenecks that limit the improvement and promotion of power battery performance. In this review, the performance characteristics, cycle life attenuation mechanism (including structural damage, gas generation, and active lithium loss, etc.), and improvement methods (including surface coating and element-doping modification) of LFP and NCM batteries are reviewed. Finally, the development prospects of this field are proposed. [ABSTRACT FROM AUTHOR]

This paper analyzes the electrical test items of the EOL testing line in automotive manufacturers. On this basis, this paper proposes and designs an integrated and automated testing strategy to deal with the problems of slow testing speed, high dependence on manual labor and low efficiency. This article mainly analyzes the various tests of the two main tests in battery EOL testing: Battery Management System (BMS) testing and electrical testing. We propose an innovative integrated solution based on various testing items, including the reception, transmission, and self-analysis of different UDS protocol messages, a unique automated electrical performance measurement scheme, and a requirement and logic design of an integrated software end based on Python. The experimental results of actual testing show that the implementation of the integrated strategy greatly reduces the complexity of the testing steps, improves the testing efficiency, and reduces errors caused by human operation. [ABSTRACT FROM AUTHOR]

Urban air mobility (UAM) is a promising transportation concept that can effectively address city traffic congestion and environmental pollution. Power batteries are used extensively in UAM vehicles, and their technical characteristics (charge rate and specific energy) are coupled with other sizing parameters to significantly impact the direct operating cost (DOC). This study develops a DOC model based on a standard flight profile and a detailed battery model to determine the impact of battery performance on UAM operations. The results reveal that for a given operating model and current battery technology, there is a narrower charge rate choice for different DOCs; a charging rate of at least 2–2.5 C is required for rational design. Advancements in specific energy are expected to reduce the DOC by 20–25% by 2035. This model reflects the impacts of battery performance on UAM operations, which is conducive to further developments in the UAM market. [ABSTRACT FROM AUTHOR]

The electric vehicle industry is developing rapidly as part of the global energy structure transformation, which has increased the importance of overcoming power battery safety issues. In this paper, first, we study the relationship between different types of vehicle faults and battery data based on the actual vehicle operation data in the big data supervisory platform of new energy vehicles. Second, we propose a method to realize the online prediction of electric vehicle battery faults, based on a Long Short-Term Memory (LSTM). Third, we carry out prediction research for two kinds of faults: low State of Charge (SOC) alarm and insulation alarm. Last, we show via experimental results that the model based on the LSTM network can effectively predict battery faults with an accuracy of more than 85%. Through this research, it is possible to complete online pre-processing of vehicle operation data and fault prediction of power batteries, improve vehicle monitoring capabilities and ensure the safety of electric vehicle use. [ABSTRACT FROM AUTHOR]

The burgeoning electric vehicle industry has become a crucial player in tackling environmental pollution and addressing oil scarcity. As these vehicles continue to advance, effective thermal management systems are essential to ensure battery safety, optimize energy utilization, and prolong vehicle lifespan. This paper presents an exhaustive review of diverse thermal management approaches at both the component and system levels, focusing on electric vehicle air conditioning systems, battery thermal management systems, and motor thermal management systems. In each subsystem, an advanced heat transfer process with phase change is recommended to dissipate the heat or directly cool the target. Moreover, the review suggested that a comprehensive integration of AC systems, battery thermal management systems, and motor thermal management systems is inevitable and is expected to maximize energy utilization efficiency. The challenges and limitations of existing thermal management systems, including system integration, control algorithms, performance balance, and cost estimation, are discussed, along with potential avenues for future research. This paper is expected to serve as a valuable reference for forthcoming research. [ABSTRACT FROM AUTHOR]

The widespread adoption and utilization of electric vehicles has been constrained by power battery performance. We proposed a fault diagnosis method for power batteries based on multiple-model fusion. The method effectively fused the advantages of various classification models and avoided the bias of a single model towards certain fault types. Firstly, we collected and sorted parameter information of the power battery during operation. Three common neural networks: back propagation (BP) neural network, convolution neural network (CNN), and long short-term memory (LSTM) neural network, were applied to battery fault diagnosis to output the fault types. Secondly, the fusion algorithm proposed in this paper determined the accurate fault type. Based on the improved voting method, the proposed fusion algorithm, named the multi-level decision algorithm, calculated the voting factors of the diagnostic results of each classification model. According to the set decision thresholds, multi-level decision voting was conducted to avoid neglecting effective classification information from minority models, which can occur with traditional voting methods. Finally, the accuracy and effectiveness of the proposed method were verified by comparing the accuracy of each classification model with the multiple model fusion algorithm. [ABSTRACT FROM AUTHOR]

The continued industrialization of new-energy vehicles has facilitated the rapid growth of the massive retired power battery drive recovery and cascade utilization industries. Improving the full lifecycle value of power batteries and recycling necessary materials has recently emerged as a hot issue. Cascade utilization, disassembly and recycling of power batteries are some key strategies to address this concern. In the context of government subsidies and extended producer responsibility, a tripartite evolutionary game model of manufacturers, third-party recyclers and cascade utilization enterprises is constructed in this study to enhance the entire lifecycle value of power batteries for the double closed-loop supply chain containing cascade utilization. Moreover, the stability of each subject’s strategy selection is analyzed, the effects of related factors on each subject’s strategy selection are examined, and the conditions for the stable evolution of the tripartite game to the equilibrium point are further discussed. The research demonstrates that: 1) increasing government subsidies and manufacturers’ reasonable formulation of internal incentive mechanisms in the supply chain are conducive to the coordinated development of the supply chain. In addition, relevant subjects jointly promote the healthy development of the cascade utilization industry; 2) the expected profits of various subjects are essential factors that affect their decision-making. Improving the profits of adopting recycled materials remanufacturing, high-level processing and large-scale cascade utilization are conducive to enhancing the comprehensive utilization level of industrial resources; 3) reducing the potential risks of the innovative development of recyclers and cascade utilization enterprises can increase the enthusiasm of both parties to promote the practical improvement of cascade utilization levels; and 4) raising the environmental treatment fees of professional battery disassembly enterprises is an effective approach to promote the resource utilization efficiency. Finally, Octave was applied for numerical simulation. Relevant countermeasures and suggestions were proposed for the coordinated and efficient development of power battery cascade utilization based on the influence relationship of various factors and equilibrium point stability conditions.

With the development of the electric vehicle industry, electric vehicles have provided more choices for people. However, the performance of electric vehicles needs improvement, which makes most consumers take a wait-and-see attitude. Therefore, finding a method that can effectively improve the performance of electric vehicles is of great significance. To improve the current performance of electric vehicles, a convex optimization algorithm is proposed to optimize the motor model and power battery parameters of electric vehicles, improving the overall performance of electric vehicles. The performance of the proposed convex optimization algorithm, dual loop DP optimization algorithm, and nonlinear optimization algorithm is compared. The results show that the hydrogen consumption of electric vehicles optimized by the convex optimization algorithm is 95.364 g. This consumption is lower than 98.165 g of the DCDP optimization algorithm and 105.236 g of the nonlinear optimization algorithm before optimization. It is also significantly better than the 125.59 g of electric vehicles before optimization. The calculation time of the convex optimization algorithm optimization is 4.9 s, which is lower than the DCDP optimization algorithm and nonlinear optimization algorithm. The above results indicate that convex optimization algorithms have better optimization performance. After optimizing the power battery using a convex optimization algorithm, the overall performance of electric vehicles is higher. Therefore, this method can effectively improve the performance of current electric vehicle power batteries, make new energy vehicles develop rapidly, and improve the increasingly serious environmental pollution and energy crisis in China. [ABSTRACT FROM AUTHOR]

Under the pressure of energy and environmental protection, new energy vehicles have become the future direction of automotive development. However, the safety performance of the power battery has always been the most critical indicator in the new energy vehicle industry. The battery will be aged in the continuous charging and discharging cycle, and the aging will cause safety hazards when it reaches a limit. A model that can predict the battery life can be obtained using Machine Learning. To obtain models that can predict power battery life relatively accurately, this paper revolves around the chaos sparrow search optimization algorithm, Random Forest, XGBoost, LightGBM, CatBoost, and NN, the importance assessment of the features, the hyperparameter search process, and the comparison of the differences and performance between the different algorithms are discussed. CatBoost has the highest prediction accuracy, with the amount of predicted data with a relative error of less than 10% being 88.44%. (a total of 10,275 data in the test set). And finally comes up with a general approach to predicting power battery life using Machine Learning. [ABSTRACT FROM AUTHOR]

Rapidly and accurately diagnosing power battery faults in new energy vehicles can significantly improve battery safety. Aiming at the collected power battery historical fault data information, a power battery fault diagnosis method based on an improved sparrow search algorithm (ISSA) optimized LSTM neural network is proposed. First, typical fault types are screened out through statistical fault sample data, and feature extraction is carried out by using wavelet packet unsupervised learning, solving the problem that long time series signal features are difficult to extract and recognize. Second, to solve the uneven distribution problem in initial population randomization, which can result in slow process of the algorithm, the initial position of the sparrow population is initialized using piecewise chaotic mapping with a homogenized distribution. Then, the population's optimal position in each iteration is perturbed using a variant of Gaussian difference, addressing the issue of the population easily converging to local optima. Finally, the hidden layer's optimal number of neurons of LSTM neural network is optimized by improving the sparrow search algorithm. Solving the problems of randomness and the difficulty in selecting the hyperparameters of the LSTM, a feature matrix is used as the input of the LSTM for model training and fault diagnosis and classification. The effectiveness of this method is verified by comparative experiments. The results indicate that the improved Sparrow search algorithm proposed can improve the capabilities of power battery fault diagnosis.

In the context of carbon neutralization, the electric vehicle and energy storage market is growing rapidly. As a result, battery recycling is an important work with the consideration of the advent of battery retirement and resource constraints, environmental factors, resource regional constraints, and price factors. Based on the theoretical research of intelligent algorithm and mathematical models, an integer programming model of urban power battery reverse supply chain scheduling was established with the goal of the highest customer satisfaction and the least total cost of logistics and distribution, to study the influence of the resources and operation status of a built city recycling center and dismantling center on the power battery reverse supply chain. The model includes vehicle load, customer demand point satisfaction range, and service capacity constraints. This study collected regional image data, conducted image analysis, and further designed an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) optimization algorithm suitable to solve the global optimization problem by introducing the improvement strategy of convergence rate, particle search, and the traditional elite individual retention. The results verified the practicability of the model, the global optimization ability of the algorithm to solve the problem, and the operation speed through comparing the results obtained from the basic algorithm. A reasonable comprehensive solution for the location and path optimization of the urban recycling center was also obtained. Multi-objective optimization was carried out in vehicle scheduling, facility construction, and customer satisfaction construction. The basic algorithm and integrated optimization software were compared. We found that the model and the scheme provided by the algorithm can significantly reduce the operation cost of the enterprise. This research provided new insights for enterprises to effectively utilize resources and optimize the reverse supply chain scheduling of an urban power battery. [ABSTRACT FROM AUTHOR]

In this paper, an acrylate-modified binder is introduced to the negative electrode of a power battery to improve its fast-charging performance. The Li+ ionic conductivity of the acrylated-modified GD1346 copolymer was twice that of commercial SN307 copolymer at room temperature. GD1346 greatly reduced the Ohm resistance and charge transfer resistance of the negative electrode from those of SN307 and enhanced the surface potential to more than 15 mV. This high performance is attributed to strong electrolyte adsorption ability and the ester groups, which favour Li+ diffusion. Consequently, the fast-charging time of the power battery is reduced from 107 to 82 min as the state of charge increases from 0 to 100%. The practicality of acrylate-modified GD1346 binder in the manufacture of large-scale batteries is also confirmed. The techniques developed in this work are expected to promote the development of efficient binders for next-generation high-energy lithium ion batteries. [ABSTRACT FROM AUTHOR]

The serpentine tube liquid cooling and composite PCM coupled cooling thermal management system is designed for 18650 cylindrical power batteries, with the maximum temperature and temperature difference of the power pack within the optimal temperature operating range as the target. The initial analysis of the battery pack at a 5C discharge rate, the influence of the single cell to cooling tube distance, the number of cooling tubes, inlet coolant temperature, the coolant flow rate, and other factors on the heat dissipation performance of the battery pack, initially determined a reasonable value for each design parameter. A control strategy is used to regulate the inlet flow rate and coolant temperature of the liquid cooling system in order to make full use of the latent heat of the composite PCM and reduce the pump's energy consumption. The simulation results show that the maximum battery pack temperature of 309.8 K and the temperature difference of 4.6 K between individual cells with the control strategy are in the optimal temperature operating range of the power battery, and the utilization rate of the composite PCM is up to 90%. [ABSTRACT FROM AUTHOR]

In order to give full play to the advantages of power battery and super-capacitor in the hybrid energy storage system (HESS) of hybrid electric vehicles (HEV), a new control strategy based on the subtractive clustering (SC) and adaptive fuzzy neural network (AFNN) was proposed to solve the problem of power distribution between the two energy sources when the driving schedule changes. Firstly, we used the SC to determine the structure of AFNN. Secondly, in order to improve the learning efficiency of AFNN, the back-propagation hybrid least square algorithm was applied to optimize the antecedent and conclusion parameters of network. Finally, the fuzzy membership function and rule set automatically generated by the neural network were used to the power distribution control of HESS in HEV. We verified the SC and AFNN control strategy by simulation and experiment based on the ADVISOR 2002 simulation software and the experimental platform, and the results show that the proposed control strategy can give full play to the advantages of HESS, and improve the energy storage performance of HEV. [ABSTRACT FROM AUTHOR]

Reliable quality control of laser welding on power batteries is an important issue due to random interference in the production process. In this paper, a quality inspection framework based on a two-branch network and conventional image processing is proposed to predict welding quality while outputting corresponding parameter information. The two-branch network consists of a segmentation network and a classification network, which alleviates the problem of large training sample size requirements for deep learning by sharing feature representations among two related tasks. Moreover, coordinate attention is introduced into feature learning modules of the network to effectively capture the subtle features of defective welds. Finally, a post-processing method based on the Hough transform is used to extract the information of the segmented weld region. Extensive experiments demonstrate that the proposed model can achieve a significant classification performance on the dataset collected on an actual production line. This study provides a valuable reference for an intelligent quality inspection system in the power battery manufacturing industry.

Thermal runaway is an essential problem to be solved urgently for electric vehicles, and the safety issues have attracted the attention of researchers. Proper thermal management systems can effectively reduce the surface temperature of battery pack and improve the uniformity of the temperature distribution, which can effectively prevent the occurrence of thermal runaway phenomenon. This paper takes the large-capacity square-shell lithium-ion battery as the research object, and conducts in-depth research on its heat production under different working conditions through simulation. A hybrid battery thermal management system based on heat pipes, microchannel liquid-cooled plates, and phase-change materials was established, and the thermal management performance under world light vehicle test cycle conditions was studied. The results indicated the temperature evolution of each battery tended to be consistent in the first 1,500 s. In the subsequent process, the discharge rate of the battery was positively correlated with the speed of the vehicle and therefore brings the corresponding temperature response. During world light vehicle test cycle, the maximum temperature difference of the lithium battery module was 3.5 K. At the end of the test, the average temperature difference of the lithium-ion battery module was 3.2 K. [ABSTRACT FROM AUTHOR]

To solve the disadvantages of the low protection grade, high weight, and high cost of the existing locomotive power battery system, this study optimizes the existing scheme and introduces the design concept of two-stage protection. The purpose of the research is to improve the protection level of the battery pack to IP68, to optimize the sheet metal power battery box structure into a more lightweight frame structure, to simplify the cooling mode of the battery pack for natural air cooling, and to improve the battery protection level and maintain the heat exchange capability. In the course of the study, a design scheme with a two-stage protection function is proposed. The numerical model analyzes the self-load, transverse load, longitudinal load, mode, and fatigue, and optimizes the layout of the power tank cell. The optimized box model was physically tested and economically compared. The results show that: (1) The maximum load stress is 128.4 MPa, which is lower than 235 MPa, the ultimate stress of the box material, and the fatigue factor of the frame box structure is 3.75, which is higher than 1.0, and it is not prone to fatigue damage. (2) Under the low-temperature heating condition, the overall temperature rise of the battery pack is 4.3 °C, which is greater than 2.3 °C under the air conditioning heat dissipation scheme. Under the high-temperature charging condition, the overall temperature rise of the battery pack is 2.0 °C, and the temperature value is the same as the temperature rise under the air conditioning cooling scheme. Under the high-temperature discharge condition, the overall temperature rise of the battery pack is 3.0 °C, and the temperature value is greater than 2.1 °C under the air conditioning heat dissipation scheme. At the same time, the temperature rise under the three working conditions is less than the 15 °C stipulated in the JS175-201805 standard. The simulation results show that the natural airflow and two-stage protection structure can provide a good temperature environment for the power battery to work. (3) The optimized box prototype can effectively maintain the structural integrity of the battery cell in the box in extreme test cases, reducing the probability of battery fire caused by battery cell deformation. (4) The power battery adopts a two-stage protection design under the battery power level, which can simultaneously achieve battery protection and prevent thermal runaway, while reducing costs. The research results provide a new concept for the design of a locomotive power battery system. (5) The weight of the optimized scheme is 2020 kg, and the original scheme is 2470 kg; thus, the reduction in weight is 450 kg. Meanwhile, the volume of the optimized scheme is 1.49 m3, and the original scheme is 1.93 m3; thus, the reduction in volume is 0.44 m3. [ABSTRACT FROM AUTHOR]

With new energy vehicles becoming the mainstream of new vehicles sold, the surge in user ownership has triggered a wave of power battery scrapping, and the environmental problems caused by improper power battery recycling are becoming more serious. It is essential to promote the development of the closed-loop supply chain (CLSC) of power batteries effectively through government subsidies under the extended producer responsibility (EPR) regime. Combining the EPR system with the battery manufacturer as the leader and the vehicle manufacturer and the retailer as the subordinates, this paper constructs and solves four models of different CLSC subsidy objects and analyzes the pricing of power batteries by different subsidy objects by using the Stackelberg game, as well as the profit change and profit distribution ratio of each CLSC participant. The results of the study showed: (1) when the unit subsidy is limited, the government should subsidize all the CLSC subjects as much as possible. (2) When the government subsidizes the remanufacturing of power batteries, the recycling rate of power batteries is higher, and the benefits of the CLSC are better than those of subsidizing other actors. (3) The change in government subsidy objects will not affect the profit distribution ratio of CLSC, mainly because the subsidy not only improves the recovery rate, but also improves the profitability of each entity. [ABSTRACT FROM AUTHOR]

Summary: Flying car is an effective transport to solve current traffic congestion. The power batteries in flying cars discharge at a high current rate in the takeoff and landing phase, evoking a severe thermal issue. Flat heat pipe (FHP) is a relatively new type of battery thermal management technology, which can effectively maintain the temperature uniformity of the battery pack. We have constructed a resistance‐based thermal model of the batteries considering the impact of the state of charge (SOC), battery temperature, and current on the battery heat generation. The FHP model is developed based on segmental heat conduction model, and integrated into the battery model to form the battery‐FHP‐coupled model for a battery module. Experiments are carried out to verify its accuracy. Then, the battery thermal performance is analyzed under the different discharging conditions including constant discharge rates and dynamic discharge rates for flying cars. Under the condition of the flying cars, the battery maximum temperature appears at the end of takeoff stage, while the maximum temperature difference appears during the forward flight segment. Moreover, different FHP heat dissipation structures are studied to further improve the battery thermal performance. The configuration with the best performance is adopted for the battery pack, and it can meet the heat dissipation requirements of the pack at a discharge rate of 3C or that of flying cars. Finally, the influence of inlet cooling air velocity and temperature on battery thermal performance is investigated. According to the research results, air velocity has little effect on the battery maximum temperature at the discharge rate of flying cars, but it can obviously affect the temperature decrease rate. Besides, the battery maximum temperature and its temperature difference develop linearly with the air temperature. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the safe maintenance of power systems and the use of secondary batteries for electric vehicles from the experimental scenarios. In engineering, the power battery module of series connection or parallel connection is conducive to the fast combination and unloading of high-voltage energy systems in the electric vehicles. However, the parallel connection is affected by inconsistency and generally uses the powerful resistance to solve the current shock. In this paper, an improved RC network lithium-iron-phosphate battery model based on parallel hysteretic voltage is proposed to achieve a safe parallel sequence and solve the high current impact in this process. Furthermore, the voltage oscillation standard deviations at different voltage levels and voltage differences are carried out, which obtains the hysteretic curve map of oscillating voltage distribution. At last, the voltage oscillation model is established by discussing the oscillating parallel characteristics, and the control strategy of the pre-charging circuit can be applied to the voltage optimization. Comparative experimental results using 32650 lithium-ion phosphate battery can effectively achieve satisfactory predicting performance with a reasonable voltage range. [ABSTRACT FROM AUTHOR]

In the past decade, in the context of the carbon peaking and carbon neutrality era, the rapid development of new energy vehicles has led to higher requirements for the performance of strike forces such as battery cycle life, energy density, and cost. Lithium-ion batteries have gradually become mainstream in electric vehicle power batteries due to their excellent energy density, rate performance, and cycle life. At present, the most widely used cathode materials for power batteries are lithium iron phosphate (LFP) and LixNiyMnzCo1−y−zO2 cathodes (NCM). However, these materials exhibit bottlenecks that limit the improvement and promotion of power battery performance. In this review, the performance characteristics, cycle life attenuation mechanism (including structural damage, gas generation, and active lithium loss, etc.), and improvement methods (including surface coating and element-doping modification) of LFP and NCM batteries are reviewed. Finally, the development prospects of this field are proposed.

Urban air mobility (UAM) is a promising transportation concept that can effectively address city traffic congestion and environmental pollution. Power batteries are used extensively in UAM vehicles, and their technical characteristics (charge rate and specific energy) are coupled with other sizing parameters to significantly impact the direct operating cost (DOC). This study develops a DOC model based on a standard flight profile and a detailed battery model to determine the impact of battery performance on UAM operations. The results reveal that for a given operating model and current battery technology, there is a narrower charge rate choice for different DOCs; a charging rate of at least 2–2.5 C is required for rational design. Advancements in specific energy are expected to reduce the DOC by 20–25% by 2035. This model reflects the impacts of battery performance on UAM operations, which is conducive to further developments in the UAM market.

This paper analyzes the electrical test items of the EOL testing line in automotive manufacturers. On this basis, this paper proposes and designs an integrated and automated testing strategy to deal with the problems of slow testing speed, high dependence on manual labor and low efficiency. This article mainly analyzes the various tests of the two main tests in battery EOL testing: Battery Management System (BMS) testing and electrical testing. We propose an innovative integrated solution based on various testing items, including the reception, transmission, and self-analysis of different UDS protocol messages, a unique automated electrical performance measurement scheme, and a requirement and logic design of an integrated software end based on Python. The experimental results of actual testing show that the implementation of the integrated strategy greatly reduces the complexity of the testing steps, improves the testing efficiency, and reduces errors caused by human operation.

The electric vehicle industry is developing rapidly as part of the global energy structure transformation, which has increased the importance of overcoming power battery safety issues. In this paper, first, we study the relationship between different types of vehicle faults and battery data based on the actual vehicle operation data in the big data supervisory platform of new energy vehicles. Second, we propose a method to realize the online prediction of electric vehicle battery faults, based on a Long Short-Term Memory (LSTM). Third, we carry out prediction research for two kinds of faults: low State of Charge (SOC) alarm and insulation alarm. Last, we show via experimental results that the model based on the LSTM network can effectively predict battery faults with an accuracy of more than 85%. Through this research, it is possible to complete online pre-processing of vehicle operation data and fault prediction of power batteries, improve vehicle monitoring capabilities and ensure the safety of electric vehicle use.