Lithium Battery Health Estimation Based on Multiple Health Factors and IPSO-LSTM Model

LI Jun; CHEN Xiaoran; XU Liang

Vehicle Engine ›› 2025, Vol. 0 ›› Issue (1) : 39-46.

LI Jun，CHEN Xiaoran，XU Liang

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Abstract

An improved particle swarm optimization(IPSO) algorithm based on multiple health factors was proposed to optimize the LSTM model for lithium-ion battery health state(SOH) estimation. Thirteen alternative health factors were extracted from the volt, current and temperature curves measured online. Pearson correlation coefficient analysis was used to obtain four health factors as the input of IPSO-LSTM model. The adaptability of selected health factors was verified by experiments and the accurate SOH prediction was achieved. The 50%, 60% and 70% data of each battery sample were taken as the training set, and the rest were taken as the testing set. Compared with the PSO-LSTM and LSTM methods, the test results showed that the MAE, RMSE and MAPE estimated by SOH were all kept within 1%, and the model had strong generalization and effectiveness.

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lithium-ion battery / prediction model / health management

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LI Jun，CHEN Xiaoran，XU Liang. Lithium Battery Health Estimation Based on Multiple Health Factors and IPSO-LSTM Model[J]. Vehicle Engine. 2025, 0(1): 39-46