Accurately estimating the battery state of health(SOH)is essential for ensuring the safe and reliable operation of battery systems of electric vehicles.However,due to the complex and variable operating conditions enco...Accurately estimating the battery state of health(SOH)is essential for ensuring the safe and reliable operation of battery systems of electric vehicles.However,due to the complex and variable operating conditions encountered in practical applications,achieving precise and physics-informed SOH estimation remains challenging.To address these problems,this paper develops a lightweight two-stage physicsinformed neural network(TSPINN)method for SOH estimation of lithium-ion batteries with different chemistries.Specifically,this paper utilizes firstly relaxation voltage data obtained after a full charge to determine the aging-related parameters of physical equivalent circuit model(ECM).Additionally,incremental capacity(IC)feature is extracted by analyzing peak values of the IC curve during the charging phase,which thereby constitutes the first stage of the proposed TSPINN,termed as physics-informed data augmentation for SOH estimation.Additionally,the physical information can be further embedded by incorporating feature knowledge related to mechanisms into the loss function,and ultimately,the second stage of the proposed TSPINN is developed,which is named the physics-informed loss function.The effectiveness of the TSPINN method was confirmed through the experimental data for LiNi_(0.86)Co_(0.11)Al_(0.03)O_(2)(NCA)and LiNi_(0.83)Co_(0.11)Mn_(0.07)O_(2)(NCM)battery materials under different temperature conditions.The final experimental results indicate that the TSPINN method achieved SOH estimation with a mean absolute error(MAE)of 0.675%,showing improvements of approximately 29.3%,60.3%,and 8.1% compared to methods using only ECM,IC,and integrated features,respectively.The results validate the effectiveness and adaptability of TSPINN,establishing it as a reliable solution for advanced battery management systems.展开更多
Battery health evaluation and management are vital for the long-term reliability and optimal performance of lithium-ion batteries in electric vehicles.Electrochemical impedance spectroscopy(EIS)offers valuable insight...Battery health evaluation and management are vital for the long-term reliability and optimal performance of lithium-ion batteries in electric vehicles.Electrochemical impedance spectroscopy(EIS)offers valuable insights into battery degradation analysis and modeling.However,previous studies have not adequately addressed the impedance uncertainties,particularly during battery operating conditions,which can substantially impact the robustness and accuracy of state of health(SOH)estimation.Motivated by this,this paper proposes a comprehensive feature optimization scheme that integrates impedance validity assessment with correlation analysis.By utilizing metrics such as impedance residuals and correlation coefficients,the proposed method effectively filters out invalid and insignificant impedance data,thereby enhancing the reliability of the input features.Subsequently,the extreme gradient boosting(XGBoost)modeling framework is constructed for estimating the battery degradation trajectories.The XGBoost model incorporates a diverse range of hyperparameters,optimized by a genetic algorithm to improve its adaptability and generalization performance.Experimental validation confirms the effectiveness of the proposed feature optimization scheme,demonstrating the superior estimation performance of the proposed method in comparison with four baseline techniques.展开更多
采用能量型储能和功率型储能组成的混合储能系统平抑光伏输出功率波动。利用小波包分解可获取更多信号细节信息的优点,综合分析光伏功率信号的幅频特性、储能的性能特点,将光伏功率信号分解,得到光伏平抑目标功率和不同类型储能的充放...采用能量型储能和功率型储能组成的混合储能系统平抑光伏输出功率波动。利用小波包分解可获取更多信号细节信息的优点,综合分析光伏功率信号的幅频特性、储能的性能特点,将光伏功率信号分解,得到光伏平抑目标功率和不同类型储能的充放电功率。充分考虑实际工程应用中实时控制对运算速度的要求,并通过阈值判断补偿滤波延迟效应。采用模糊控制方法对功率型储能的荷电状态(state of charge,SOC)进行自适应控制,实现功率的优化分配,提高平抑效果。算例结果表明,所提控制策略能够充分利用不同类型储能的性能优势有效平抑光伏输出功率波动。展开更多
State of health(SOH)estimation is important for a lithium-ion battery(LIB)health state management system,and accurate estimation of SOH is influenced by the degree of degradation of the LIB.However,considering the com...State of health(SOH)estimation is important for a lithium-ion battery(LIB)health state management system,and accurate estimation of SOH is influenced by the degree of degradation of the LIB.However,considering the complex electrochemical reactions within Li electrons and the influence of many external factors on internal reactions,it is difficult to accurately estimate the SOH based on the surface state characteristics of the battery(including current,voltage,and temperature).Thus,in this study,the knowledge graph method is employed to analyze keyword co-occurrences and citations in the literature on LIB degradation and SOH estimation to determine research hotspots.Based on the research trends,findings regarding the internal and external degradation mechanisms and influencing factors of(LIBs)are reorganized,and chemical and physical degradation processes,including solid electrolyte interface(SEI)layer formation,fracture,Li plating,and dendrite formation,are systematically introduced based on the modeling perspective.The interrelationships between these degradation factors and their effects on capacity and power decay as well as their correlation with SOH estimation are evaluated.Additionally,a comparative analysis of existing SOH estimation methods is presented,and the applicable scenarios and technical problems of each method are summarized.The key issues such as model simplification,estimation methods based on random data,and second-life SOH are also analyzed and discussed.The results show that the estimation results of methods mixing multiple models tend to be more accurate.Finally,the development trend of SOH estimation methods under complex degradation conditions and usage scenarios is analytically discussed.展开更多
基金supported by the Scientific Research and Innovation Team Program of Sichuan University of Science and Engineering(No.SUSE652B005)Anhui Province Applied Peak Discipline Mechanical Engineering(No.XK-XJGF004)。
文摘Accurately estimating the battery state of health(SOH)is essential for ensuring the safe and reliable operation of battery systems of electric vehicles.However,due to the complex and variable operating conditions encountered in practical applications,achieving precise and physics-informed SOH estimation remains challenging.To address these problems,this paper develops a lightweight two-stage physicsinformed neural network(TSPINN)method for SOH estimation of lithium-ion batteries with different chemistries.Specifically,this paper utilizes firstly relaxation voltage data obtained after a full charge to determine the aging-related parameters of physical equivalent circuit model(ECM).Additionally,incremental capacity(IC)feature is extracted by analyzing peak values of the IC curve during the charging phase,which thereby constitutes the first stage of the proposed TSPINN,termed as physics-informed data augmentation for SOH estimation.Additionally,the physical information can be further embedded by incorporating feature knowledge related to mechanisms into the loss function,and ultimately,the second stage of the proposed TSPINN is developed,which is named the physics-informed loss function.The effectiveness of the TSPINN method was confirmed through the experimental data for LiNi_(0.86)Co_(0.11)Al_(0.03)O_(2)(NCA)and LiNi_(0.83)Co_(0.11)Mn_(0.07)O_(2)(NCM)battery materials under different temperature conditions.The final experimental results indicate that the TSPINN method achieved SOH estimation with a mean absolute error(MAE)of 0.675%,showing improvements of approximately 29.3%,60.3%,and 8.1% compared to methods using only ECM,IC,and integrated features,respectively.The results validate the effectiveness and adaptability of TSPINN,establishing it as a reliable solution for advanced battery management systems.
文摘Battery health evaluation and management are vital for the long-term reliability and optimal performance of lithium-ion batteries in electric vehicles.Electrochemical impedance spectroscopy(EIS)offers valuable insights into battery degradation analysis and modeling.However,previous studies have not adequately addressed the impedance uncertainties,particularly during battery operating conditions,which can substantially impact the robustness and accuracy of state of health(SOH)estimation.Motivated by this,this paper proposes a comprehensive feature optimization scheme that integrates impedance validity assessment with correlation analysis.By utilizing metrics such as impedance residuals and correlation coefficients,the proposed method effectively filters out invalid and insignificant impedance data,thereby enhancing the reliability of the input features.Subsequently,the extreme gradient boosting(XGBoost)modeling framework is constructed for estimating the battery degradation trajectories.The XGBoost model incorporates a diverse range of hyperparameters,optimized by a genetic algorithm to improve its adaptability and generalization performance.Experimental validation confirms the effectiveness of the proposed feature optimization scheme,demonstrating the superior estimation performance of the proposed method in comparison with four baseline techniques.
文摘采用能量型储能和功率型储能组成的混合储能系统平抑光伏输出功率波动。利用小波包分解可获取更多信号细节信息的优点,综合分析光伏功率信号的幅频特性、储能的性能特点,将光伏功率信号分解,得到光伏平抑目标功率和不同类型储能的充放电功率。充分考虑实际工程应用中实时控制对运算速度的要求,并通过阈值判断补偿滤波延迟效应。采用模糊控制方法对功率型储能的荷电状态(state of charge,SOC)进行自适应控制,实现功率的优化分配,提高平抑效果。算例结果表明,所提控制策略能够充分利用不同类型储能的性能优势有效平抑光伏输出功率波动。
基金supported by the National Key Research and Development Program of China(Grant No.2021YFE0192900)the Humanities and Social Sciences Youth Fund of Ministry of Education(18YJCZH110)+2 种基金the Natural Science Foundation of Shaanxi Province(Grant No.2023-JC-QN-0664)the Third Batch of Youth Joint Scientific Research Team Construction Project of Zhejiang Institute of Communications(2022QNLH05)the Fundamental Research Funds for the Central Universities(Grant Nos.300102222113,00102223204)。
文摘State of health(SOH)estimation is important for a lithium-ion battery(LIB)health state management system,and accurate estimation of SOH is influenced by the degree of degradation of the LIB.However,considering the complex electrochemical reactions within Li electrons and the influence of many external factors on internal reactions,it is difficult to accurately estimate the SOH based on the surface state characteristics of the battery(including current,voltage,and temperature).Thus,in this study,the knowledge graph method is employed to analyze keyword co-occurrences and citations in the literature on LIB degradation and SOH estimation to determine research hotspots.Based on the research trends,findings regarding the internal and external degradation mechanisms and influencing factors of(LIBs)are reorganized,and chemical and physical degradation processes,including solid electrolyte interface(SEI)layer formation,fracture,Li plating,and dendrite formation,are systematically introduced based on the modeling perspective.The interrelationships between these degradation factors and their effects on capacity and power decay as well as their correlation with SOH estimation are evaluated.Additionally,a comparative analysis of existing SOH estimation methods is presented,and the applicable scenarios and technical problems of each method are summarized.The key issues such as model simplification,estimation methods based on random data,and second-life SOH are also analyzed and discussed.The results show that the estimation results of methods mixing multiple models tend to be more accurate.Finally,the development trend of SOH estimation methods under complex degradation conditions and usage scenarios is analytically discussed.
