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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]

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

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

• 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]

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

The charging and discharging process of electric vehicle power batteries generates a large amount of heat, and the development of an efficient heat dissipation design is conducive to improving the efficiency and safety of the battery packs. A combination of cold plate and battery packs was used for the liquid cooling heat dissipation design of the battery packs. The critical maximum temperature was set to 50 ℃ here and the results showed that the average battery temperature was lowest and the temperature uniformity was better when the cold plate and battery packs were arranged in a side-to-side lateral direction. The minimum required mass flow rate was 0. 05 kg / s for the inter-phase lateral model, while the inter-phase upward model had the lowest pressure drop. Therefore, a structure with an interphase arrangement of power battery and cold plates can be preferred for the thermal design of the battery packs. [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]

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

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

In order to better process and dispose of the power batteries that are scrapped after cascade utilization, and help the "double carbon" goal, it is necessary to explore the costs and benefits of various stakeholders in the reverse logistics process of these power batteries. By establishing an evolutionary game model among the government, cascade utilization enterprises, and recycling businesses under the "dual-carbon" goal, we analyze the local stability of the equilibrium point and simulate the evolutionary process of each gaming agent. We also discuss the impact of key parameters on the behavioral evolution of the participating parties. Research shows that: (1) Environmental benefits significantly influence the strategic choices of the government and enterprises involved in cascade utilization. When environmental benefits decrease, the government tends to lean towards relaxed regulation, while enterprises in cascade utilization are more inclined to opt for third-party recycling strategies. (2) Increasing the probability of government inspections can encourage enterprises in cascade utilization and recyclers to choose participation in recycling as a stable strategy, but their sensitivity to strategic choices under the same conditions is different. Therefore, regulatory policies need to be differentiated according to the type of enterprise, especially increasing the regulatory penalties for enterprises in cascade utilization. (3) When the total cost of the recycling channel jointly established by enterprises in cascade utilization and recyclers remains unchanged, the variation in the costsharing coefficient does not significantly affect the stability of the system. [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]

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

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

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

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

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

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.

Battery safety issue is a key factor that hindered the reuse of retired batteries on new energy vehicles, while charge state, voltage and temperature are important parameters to judge the safety state of batteries. Based on this, a joint fault diagnosis of battery based on real vehicle data was proposed. Firstly, the data was obtained from the real vehicle data platform, and the feature extraction was carried out by data preprocessing and dung beetle optimizes K-nearest neighbor ( DPO-KNN) algorithm. Then, the extracted features were input into the established autoregressive integrated moving average model, autoregressive integrated moving average model (ARIMA) fault diagnosis model to realize real-time diagnosis and accurate location for low and overvoltage of battery cells. Finally, voltage, state of charge ( SOC) and temperature were combined to determine whether there was a risk of triggering thermal runaway, and different alarm levels were issued according to the degree of danger. It can not only accurately locate the abnormal position of the monomer, but also alarm 9 s in advance, effectively preventing the risk of thermal runaway of the battery. It achieved a good effect in engineering practical application, and lied a foundation for the future step recycling of power batteries and the research and development of safety warning platform. [ABSTRACT FROM AUTHOR]

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]

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

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

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]

As large quantities of power batteries go off the market, a multi-objective planning model of power battery reverse logistic network is built to minimize risks and costs with full consideration of risks, uncertainties and multi-processing techniques. Expected interval, expected value ad fuzzy chance constraint methods are employed to transform the model deterministically. The classic MW solution method is improved by optimizing the search space, constructing marginal rates of change and new transformation function. Our experimental results show the model effectively controls the risk and achieves high economy. The improved MW method is markedly superior to the MW method in terms of the number of Pareto optimal solutions, interaction optimization efficiency and solution time. Thus, it provides the theoretical basis and technical support for risk control decision of power battery reverse logistic network in uncertain circumstances of limited information. [ABSTRACT FROM AUTHOR]

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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