Online Lithium-ion Battery Capacity Estimation Based on Random Charging Data

doi:10.3901/JME.2023.22.100

Abstract

Abstract: As the core component of electric vehicles, accurate online estimation of lithium-ion battery capacity is crucial to ensure the safe and stable operation of electric vehicles. Combining aging characteristics and data-driven algorithms to achieve battery capacity estimation is a research hotspot. However, the charging and discharging conditions of electric vehicles are random, which makes it difficult to obtain specific aging characteristics stably. To this end, an online battery capacity estimation method based on random charging data is proposed. First, the cumulative electric quantity, cumulative energy, average value and variance of electric quantity of random charging segments are extracted as features by analyzing the charging data of different aging batteries. Then, the correlation between the extracted features and the battery capacity is evaluated based on the Pearson correlation coefficient and the grey correlation degree analysis method. Finally, an online battery capacity estimation method based on improved kernel extreme learning machine is proposed. Compared with other data-driven methods, the proposed method has higher estimation accuracy, with an average capacity estimation error of 1.38% under random voltage window. In particular, the error is only 0.48% under the optimal window. The comparison results show that the constructed estimation model is stable under different sampling frequencies. Meanwhile, only a small number of new samples can be used to accurately estimate the battery capacity under different charging conditions. The proposed method has strong generalization performance and high practical value.

Key words: lithium-ion battery, capacity estimation, random charging data, data-driven, extreme learning machine

CLC Number:

TM912

GU Pingwei, DUAN Bin, KANG Yongzhe, ZHANG Chenghui, DU Chunshui. Online Lithium-ion Battery Capacity Estimation Based on Random Charging Data[J]. Journal of Mechanical Engineering, 2023, 59(22): 100-110.

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