Battery state of health prediction based on voltage intervals, BP neural network and genetic algorithm

ABSTRACTAccurate prediction of lithium ion (li-ion) battery capacity is of great significance to battery health status management. In this paper, the different discharge time corresponding to the equal voltage interval is taken as the health factor. Three highly correlated health factors are extracted from the battery discharge curve, and the Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) is used to estimate the battery capacity accurately and quickly. Firstly, health factors related highly to battery capacity from the battery cycle life test are extracted, and the selected health factors are analyzed using the Spearman correlation coefficient and Pearson correlation coefficient. Secondly, this paper analyzes the prediction effect of different combinations of selected health factors using BPNN optimized by a genetic algorithm. Then, to verify the superiority of the proposed optimization algorithm, different optimization algorithms are used to adjust and optimize the parameters of BPNN automatically, and the experimental data are used for comparative analysis. Finally, the GA-BP is compared with other common battery capacity prediction methods. The results show that the GA-BP neural network prediction model can accurately and effectively predict the capacity of li-ion battery when using selected health factors.KEYWORDS: Lithium ion batterybattery health status managementdischarge curvegenetic algorithmback propagation neural network Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data are available from the corresponding author on reasonable request.Additional informationFundingThis work is supported by National Natural Science Foundation of China (NSFC, U1966602, 52377161, 52007158), Excellent Young Scientists Fund of China (51922090), Southwest Jiaotong University new interdisciplinary cultivation project (YH1500112432273 and YH15001124322105), and Fundamental Research Funds for the Central Universities (A0920502052301-170).