Generalized Data-driven SOH Estimation Method for Battery Systems

doi:10.3901/JME.2022.24.253

Abstract

Abstract: Accurate and reliable battery state of health estimation is the key to ensuring the safe operation of lithium-ion batteries, and provides a reference for failure warning. A general method to estimate the state of health of both battery cells and battery packs is proposed. Firstly, a method for extracting high-quality health indicators of battery cells based on partial charge or discharge data is proposed to ensure the high correlation between health indicators and battery capacity and the online availability of the health indicators. Secondly, a feature generation strategy that considers the capacity attenuation and inconsistency of the battery pack is proposed. The final fusion feature is extracted by using principal component analysis to reduce the dimensionality of the feature matrix. The dual time scale filtering and battery pack equivalent circuit model are combined to broaden the extraction under dynamic discharge conditions. Then, based on the framework of the Gaussian process regression, an improved Gaussian kernel function is proposed considering the overall relationship and local changes of the health indicators and capacity attenuation. Finally, multiple experimental data sets are used to verify the generalization ability of the proposed method under different application conditions. The estimation results show that the proposed method has an estimation error of less than 1.28% for battery cells under constant current discharge conditions, and an estimation error of less than 1.82% for battery cells under dynamic working conditions with changeable environmental temperatures. The verification results for series battery packs show that it can be used in various application scenarios with estimation errors all less than 1.43%. The accuracy of and adaptability in a wide range of application scenarios of battery state of health estimation for battery systems are improved.

Key words: lithium-ion battery, state of health, health indicators, Gaussian process regression, state estimation

CLC Number:

TM912

CHE Yunhong, DENG Zhongwei, LI Jiacheng, XIE Yi, HU Xiaosong. Generalized Data-driven SOH Estimation Method for Battery Systems[J]. Journal of Mechanical Engineering, 2022, 58(24): 253-263.

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