Automatic Feature Extraction-Enabled Lithium-Ion Battery Capacity Estimation Using Random Fragmented Charging Data

被引:0
作者
Zhou, Ziyou [1 ,2 ]
Liu, Yonggang [1 ,2 ]
Zhao, Zhigang [3 ]
Xia, Huan [3 ]
Chen, Zheng [4 ,5 ]
Zhang, Yuanjian [6 ]
机构
[1] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400000, Peoples R China
[2] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400000, Peoples R China
[3] Beijing Inst Space Launch Technol, Beijing 100000, Peoples R China
[4] Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming 650500, Peoples R China
[5] Queen Mary Univ London, Sch Engn & Mat Sci, London E1 4NS, England
[6] Loughborough Univ, Dept Aeronaut & Automot Engn, Loughborough LE11 3TU, Leics, England
来源
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION | 2024年 / 10卷 / 04期
基金
中国国家自然科学基金;
关键词
Estimation; Feature extraction; Data models; Voltage; Lithium-ion batteries; Computational modeling; Correlation; Capacity estimation; deep-learning model; health diagnosis; lithium battery; STATE-OF-HEALTH; PREDICTION; MANAGEMENT; SYSTEM;
D O I
10.1109/TTE.2024.3357728
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Nowadays, health diagnosis for lithium-ion batteries is critical to ensure their normal and safe operations. However, precise estimation of battery capacity is a challenging task, especially under complex and varying operation conditions. To tackle this problem, we propose an automatic feature extraction technique that utilizes random fragmented charging data to achieve precise capacity estimation across diverse operational scenarios. The automatic feature extraction is achieved by a deep autoencoder (DAE) model and can be applied to other conditions without additional training, justifying its generalization performance. Through a comprehensive exploration of the capacity estimation performance across various input data segments, we introduce a novel approach to select preferable input data and develop a universal estimation model for achieving accurate capacity estimation. Additionally, the Bayesian neural network (NN) is exploited in the universal estimation model to quantify the uncertainty of the estimated results. Experimental datasets from three distinct types of batteries operating under diverse conditions are applied to examine the performance of the proposed method. The results manifest that our method yields robust and precise capacity estimation under various charging conditions.
引用
收藏
页码:8845 / 8856
页数:12
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