State-of-health Estimate for Lithium-ion Battery Using Information Entropy and PSO-LSTM

doi:10.3901/JME.2022.10.180

Abstract

Abstract: In order to address the shortcoming of the existing lithium-ion battery pack state of health(SOH) estimation methods, a SOH estimation approach for lithium-ion battery pack using information entropy and particle swarm optimization(PSO) to optimize the long short-term memory(LSTM) neural network is proposed. The data of the information entropy and the average temperature of each cell terminal voltage in the lithium-ion battery pack during the constant current-constant voltage charging stage are utilized to extract the mapping relationship between the voltage entropy, average temperature, and SOH of the lithium-ion battery pack using PSO-LSTM, and then establish the lithium-ion battery pack SOH estimation model. The measured aging data of lithium-ion battery pack in the laboratory are employed to verify the validity of the presented method. The results show that the developed approach can accurately estimate the SOH of the lithium-ion battery pack with the average estimation error within 1%. Meanwhile, in order to verify the proposed method can be extended to lithium-ion batteries, the accelerated aging data of lithium-ion batteries measured by National Aeronautics and Space Administration(NASA) to test again with the average estimation error within 0.7%. The compared experiment is designed for the battery pack and cells, which further demonstrates that the suggested method offers a favorable estimation performance.

Key words: lithium-ion battery pack, state of health, information entropy, particle swarm optimization, long short-term memory neural network

CLC Number:

TM910

ZHANG Chaolong, ZHAO Shaishai, HE Yigang. State-of-health Estimate for Lithium-ion Battery Using Information Entropy and PSO-LSTM[J]. Journal of Mechanical Engineering, 2022, 58(10): 180-190.

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