Accurately estimating the State of Health(SOH)of batteries is of great significance for the stable operation and safety of lithiumbatteries.This article proposes amethod based on the combination of Capacity Incrementa...Accurately estimating the State of Health(SOH)of batteries is of great significance for the stable operation and safety of lithiumbatteries.This article proposes amethod based on the combination of Capacity Incremental Curve Analysis(ICA)andWhale Optimization Algorithm-Radial Basis Function(WOA-RBF)neural network algorithm to address the issues of low accuracy and slow convergence speed in estimating State of Health of batteries.Firstly,preprocess the battery data to obtain the real battery SOH curve and Capacity-Voltage(Q-V)curve,convert the Q-V curve into an IC curve and denoise it,analyze the parameters in the IC curve that may serve as health features;Then,extract the constant current charging time of the battery and the horizontal and vertical coordinates of the two IC peaks as health features,and perform correlation analysis using Pearson correlation coefficient method;Finally,theWOA-RBF algorithmwas used to estimate the battery SOH,and the training results of LSTM,RBF,and PSO-RBF algorithms were compared.The conclusion was drawn that theWOA-RBF algorithm has high accuracy,fast convergence speed,and the best linearity in estimating SOH.The absolute error of its SOHestimation can be controlled within 1%,and the relative error can be controlled within 2%.展开更多
为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯...为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯网络的电池荷电状态(state of charge,SOC)和健康状态(state of health,SOH)联合估计方法。应用多阶电阻-电容电路(resistor-capacitance circuit,RC)模型、使用节点-支路框架构建电池的等效电路模型,通过基尔霍夫定律与欧姆定律对二阶RC电池等效电路模型中的并联回路进行电气特性分析,构建空间状态方程及等效输出方程;对构建的状态方程进行离散化处理,分别定义并联独立回路离散化零输入响应、零状态响应,分析离散化电池模型状态空间方程;将专家打分法引入TOPSIS算法中进行电池SOC量化估计,结合融入模糊尺度的贝叶斯网络,在相同时间分布尺度下通过电池SOH值计算电池观测样本中对应的SOC值,实现电池SOH与SOC联合估计。实验结果表明:所提方法可有效估计不同离散空间尺度下的电池SOC和SOH结果,估计方法具有良好的准确性与较高的精度。展开更多
健康状态(state of health,SOH)是电池管理系统的重要参考依据,准确的SOH估计对保证电池安全稳定运行具有重大意义,其中提取可靠有效的健康特征描述电池老化状态以及构建精确稳定的估计模型是目前面临的主要问题。为了提高SOH估计精度,...健康状态(state of health,SOH)是电池管理系统的重要参考依据,准确的SOH估计对保证电池安全稳定运行具有重大意义,其中提取可靠有效的健康特征描述电池老化状态以及构建精确稳定的估计模型是目前面临的主要问题。为了提高SOH估计精度,提出了一种基于模糊熵和粒子滤波(particle filter,PF)的锂离子电池SOH估计方法。首先,通过分析电池老化过程中的放电电压数据,提取模糊熵值作为电池的老化特征;其次,基于代谢灰色模型(metabolic grey model,MGM)和时间卷积网络(temporal convolutional network,TCN)构建描述锂电池老化特征的非参数状态空间模型;最后,通过PF实现锂电池SOH的闭环估计。此外,利用NASA锂电池数据集对所提出的SOH估计方法进行了验证,并与该领域其他方法进行对比实验。结果表明,所提方法最大估计误差在5%左右,相比于同类方法其估计精度提升了约50%,且在不同训练周期数条件下表现出较好的鲁棒性,验证了所提方法的可行性与优越性。展开更多
为提高对动力电池的荷电状态(state of charge, SOC)估算精度、动力电池的健康状态(state of health, SOH)对锂电池性能的影响,提出一种扩展卡尔曼滤波(extended kalman filtering, EKF)联合估算算法。根据现有的实验数据,分析锂电池特...为提高对动力电池的荷电状态(state of charge, SOC)估算精度、动力电池的健康状态(state of health, SOH)对锂电池性能的影响,提出一种扩展卡尔曼滤波(extended kalman filtering, EKF)联合估算算法。根据现有的实验数据,分析锂电池特性,构建二阶RC等效电路模型,并进行参数辨识,搭建MATLAB仿真平台联合EKF算法进行SOC估算,将仿真结果与真实数据进行对比,结果表明,EKF联合估算SOC比EKF估算SOC误差精度约高1.2%,且抗干扰能力更强。展开更多
基金funded by the Basic Science(Natural Science)Research Project of Colleges and Universities in Jiangsu Province,grant number 22KJD470002.
文摘Accurately estimating the State of Health(SOH)of batteries is of great significance for the stable operation and safety of lithiumbatteries.This article proposes amethod based on the combination of Capacity Incremental Curve Analysis(ICA)andWhale Optimization Algorithm-Radial Basis Function(WOA-RBF)neural network algorithm to address the issues of low accuracy and slow convergence speed in estimating State of Health of batteries.Firstly,preprocess the battery data to obtain the real battery SOH curve and Capacity-Voltage(Q-V)curve,convert the Q-V curve into an IC curve and denoise it,analyze the parameters in the IC curve that may serve as health features;Then,extract the constant current charging time of the battery and the horizontal and vertical coordinates of the two IC peaks as health features,and perform correlation analysis using Pearson correlation coefficient method;Finally,theWOA-RBF algorithmwas used to estimate the battery SOH,and the training results of LSTM,RBF,and PSO-RBF algorithms were compared.The conclusion was drawn that theWOA-RBF algorithm has high accuracy,fast convergence speed,and the best linearity in estimating SOH.The absolute error of its SOHestimation can be controlled within 1%,and the relative error can be controlled within 2%.
文摘为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯网络的电池荷电状态(state of charge,SOC)和健康状态(state of health,SOH)联合估计方法。应用多阶电阻-电容电路(resistor-capacitance circuit,RC)模型、使用节点-支路框架构建电池的等效电路模型,通过基尔霍夫定律与欧姆定律对二阶RC电池等效电路模型中的并联回路进行电气特性分析,构建空间状态方程及等效输出方程;对构建的状态方程进行离散化处理,分别定义并联独立回路离散化零输入响应、零状态响应,分析离散化电池模型状态空间方程;将专家打分法引入TOPSIS算法中进行电池SOC量化估计,结合融入模糊尺度的贝叶斯网络,在相同时间分布尺度下通过电池SOH值计算电池观测样本中对应的SOC值,实现电池SOH与SOC联合估计。实验结果表明:所提方法可有效估计不同离散空间尺度下的电池SOC和SOH结果,估计方法具有良好的准确性与较高的精度。
文摘为提高对动力电池的荷电状态(state of charge, SOC)估算精度、动力电池的健康状态(state of health, SOH)对锂电池性能的影响,提出一种扩展卡尔曼滤波(extended kalman filtering, EKF)联合估算算法。根据现有的实验数据,分析锂电池特性,构建二阶RC等效电路模型,并进行参数辨识,搭建MATLAB仿真平台联合EKF算法进行SOC估算,将仿真结果与真实数据进行对比,结果表明,EKF联合估算SOC比EKF估算SOC误差精度约高1.2%,且抗干扰能力更强。
