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
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%.
