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• 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]

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.)

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.

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]

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 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 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.)

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

The 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]

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]

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.

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]

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 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.)

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.

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.

• A novel SSPCM/TEC/seawater cooling system for marine power batteries was proposed. • Seawater cooling without metal pipes solution was proposed. • SSPCM was used as seawater channels in the cooling systems. • Thermoelectric coolers were used to optimize the cooling system. For green shipping, lithium-ion battery-powered ships are rapidly developing. Compared with power batteries for electric vehicles, the large volume and capacity of cells in electric ships make it difficult to dissipate heat, thus requiring efficient cooling. Herein, a novel shape-stabilized phase change material (SSPCM) channel/thermoelectric cooler (TEC) cooling system for marine power batteries was proposed and experimentally studied. It uses TEC for localized cooling and seawater flowing inside the SSPCM as coolant. First, the arrangement of the TECs was experimentally determined. Then, the cooling performance of the system was verified by comparative experiments. The results show that, compared to the cooling system without TECs, the maximum temperature difference was reduced by 28.8 %, with the maximum temperature and temperature difference of 49.8 °C and 3.7 °C, respectively. Compared with the single cooling method, the maximum temperature and temperature difference were reduced by 15.5 % and 53.3 %, respectively. Finally, the experiments were conducted to explore the impact of various parameters on the cooling performance of the system. The unique combination of SSPCM channel, seawater, and TEC provides a new strategy for efficiently cooling marine power batteries at high charge/discharge rates. [Display omitted] [ABSTRACT FROM AUTHOR]

Cell voltage inconsistency in a battery pack is an important signal released by the deterioration of battery performance. In this paper, voltage inconsistency is categorized into static inconsistency and dynamic inconsistency, and the latter contains progressive fluctuation fault and sudden fluctuation fault. For voltage dynamic inconsistency, this paper innovatively proposes a fault diagnosis and type identification method based on weighted Euclidean distance evaluation and statistical analysis. Specifically, firstly, the Euclidean distance evaluation method is used to quantify the cumulative length of the voltage curve of each individual cell in the time window, and at the same time, different forgetting factors are weighted on the voltage line segments to mitigate the fault diagnostic delay and memory effect defects of the time window. Then, a voltage abnormality evaluation coefficient is introduced to characterize the degree of inconsistent fluctuation of the cell voltage, and statistical methods are used to find a reasonable threshold. Further, fault type identification algorithm layer is conducted to identify the type of cell voltage fluctuation by using the optimized correlation coefficient method while the cell is detected by the fault diagnosis algorithm layer. Finally, the effectiveness of the proposed fault diagnosis strategy is verified by experimental data, and an online platform is utilized to obtain voltage data with different fault characteristics to test the practical application of the method. By comparing the proposed method with various data-driven methods with the same data evaluation significance, the results show that the method in this paper is more robust to static voltage inconsistency and the length of the time window, and is capable to recognize the various data patterns of potential threats, with higher accuracy and computational efficiency. • Euclidean distance evaluation is used for cell voltage inconsistency fault diagnosis. • Forgetting factor is used to mitigate fault diagnosis delay and memory effect defects of time window. • Optimized correlation coefficient is used to isolate pattern of cell voltage inconsistent fluctuations. • The accuracy is more than 99%. [ABSTRACT FROM AUTHOR]