电力系统保护与控制2024，Vol.52Issue(19)：74-84,11.DOI:10.19783/j.cnki.pspc.240248
基于GA-SA-BP神经网络的锂电池健康状态估算方法
Lithium battery state of health estimation method based on a GA-SA-BP neural network
摘要
Abstract
The state of health(SOH)of lithium batteries can characterize their aging status,and accurately estimating SOH is crucial for reliable operation.A GA-SA-BP neural network algorithm is proposed to improve the accuracy of SOH estimation to solve the problem of low convergence efficiency and susceptibility to local optima of BP neural networks optimized by simulated annealing(SA)and genetic algorithms(GA),which cannot reach the global optimum.First,the correlation between various health indicators(HI)and SOH in NASA's publicly available dataset is analyzed,and the four HI values of lithium battery output voltage,output current,capacity,and equal voltage drop discharge time with higher correlation with SOH is selected as input values for the BP neural network to improve the accuracy of SOH estimation.Secondly,the GA-SA-BP neural network algorithm is proposed to estimate SOH,and the global optimal solution is found by jumping out of the local optimum when trapped in order to further improve the accuracy of SOH estimation.Finally,the results obtained on the NASA lithium battery dataset and lithium battery experimental testing platform indicate that the proposed approach improves the accuracy of SOH estimation compared to traditional BP neural networks,GA-BP neural networks,and SA-BP neural networks,and remains effective even in the absence of some data.关键词
锂电池/健康状态估算/神经网络/健康因子Key words
lithium battery/estimate of state of health/neural network/health indicator引用本文复制引用
吴青峰,杨艺涛,刘立群,胡秀芳,薄利明,杨杰豹..基于GA-SA-BP神经网络的锂电池健康状态估算方法[J].电力系统保护与控制,2024,52(19):74-84,11.基金项目
This work is supported by the National Key R&D Program of China(No.2018YFA0707305). 国家重点研发计划项目资助(2018YFA0707305) （No.2018YFA0707305）
山西省基础研究计划面上项目资助(202203021221153) （202203021221153）
阳泉市应用基础研究计划项目资助(2022JH059) （2022JH059）
山西省研究生教育创新项目资助(2024KY659) （2024KY659）
