LITHIUM-ION BATTERY STATE OF HEALTH ESTIMATION BASED ON INTERPRETABLE SHAPELETS

Li Yihuan; Zheng Hanjin; Wang Wei; Wang Yanxia

doi:10.19912/j.0254-0096.tynxb.2024-1209

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 76-84. DOI: 10.19912/j.0254-0096.tynxb.2024-1209

LITHIUM-ION BATTERY STATE OF HEALTH ESTIMATION BASED ON INTERPRETABLE SHAPELETS

Li Yihuan¹, Zheng Hanjin¹, Wang Wei¹, Wang Yanxia²

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Abstract

In response to the problems of poor interpretability and strong dependence on data for battery state of health（SOH） estimation of lithium-ion batteries based on machine learning, this paper proposes a SOH estimation method for lithium batteries with interpretability. First of all, this work analyzes that as batteries age, the distance between the charging voltage and the first charging voltage exhibits a good trend, and proposes a distance metric based on the concept of Shapelets that can capture the degradation trend of the battery, and further determines the range of the Shapelets candidate set through correlation analysis to improve the efficiency of the feature extraction. The selection of Shapelets is carried out in combination with the BP model optimized by the subtraction-average-based optimizer (SABO) algorithm. Finally, the Shapelets-based SABO-BP model is designed to realize the effective estimation of battery SOH. The proposed method is validated on the Stanford-MIT dataset, and batteries with different charging strategies are selected for test, the MAE of the battery SOH estimation are all maintained within 0.5%, and can reach as low as 0.19%; the RMSE are also all maintained within 0.6%, and can reach as low as 0.26%; and the R² stays above 0.98 and reaches up to 0.995. The experimental results show that the proposed method is able to accurately predict the SOH of lithium batteries with limited data, which confirms the generalization and practical value of the proposed algorithm.

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lithium-ion batteries / state estimation / neural network models / Shapelets / subtraction-average-based optimizer

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Li Yihuan, Zheng Hanjin, Wang Wei, Wang Yanxia. LITHIUM-ION BATTERY STATE OF HEALTH ESTIMATION BASED ON INTERPRETABLE SHAPELETS[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 76-84 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1209

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