Early Prognostics of Lithium-Ion Battery Pack Health

Jiwei Wang, Zhongwei Deng, Kaile Peng, Xinchen Deng, Lijun Xu, Guoqing Guan and Abuliti Abudula
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Jiwei Wang: Graduate School of Science and Technology, Hirosaki University, Hirosaki 036-8560, Japan
Zhongwei Deng: College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
Kaile Peng: College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
Xinchen Deng: College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
Lijun Xu: Xinjiang Coal Mine Electromechanical Engineering Technology Research Center, Xinjiang Institute of Engineering, Urumqi 830023, China
Guoqing Guan: Graduate School of Science and Technology, Hirosaki University, Hirosaki 036-8560, Japan
Abuliti Abudula: Graduate School of Science and Technology, Hirosaki University, Hirosaki 036-8560, Japan

Sustainability, 2022, vol. 14, issue 4, 1-21

Abstract: Accurate health prognostics of lithium-ion battery packs play a crucial role in timely maintenance and avoiding potential safety accidents in energy storage. To rapidly evaluate the health of newly developed battery packs, a method for predicting the future health of the battery pack using the aging data of the battery cells for their entire lifecycles and with the early cycling data of the battery pack is proposed. Firstly, health indicators (HIs) are extracted from the experimental data, and high correlations between the extracted HIs and the capacity are verified by the Pearson correlation analysis method. To predict the future health of the battery pack based on the HIs, degradation models of HIs are constructed by using an exponential function, long short-term memory network, and their weighted fusion. The future HIs of the battery pack are predicted according to the fusion degradation model. Then, based on the Gaussian process regression algorithm and battery pack data, a data-driven model is constructed to predict the health of the battery pack. Finally, the proposed method is validated with a series-connected battery pack with fifteen 100 Ah lithium iron phosphate battery cells. The mean absolute error and root mean square error of the health prediction of the battery pack are 7.17% and 7.81%, respectively, indicating that the proposed method has satisfactory accuracy.

Keywords: lithium-ion battery pack; state of health; health indicators; fusion model; Gaussian process regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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