Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML

Chenqiang Luo, Zhendong Zhang, Dongdong Qiao, Xin Lai, Yongying Li and Shunli Wang
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Chenqiang Luo: College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Zhendong Zhang: College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Dongdong Qiao: School of Automotive Studies, Tongji University, Shanghai 201804, China
Xin Lai: College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Yongying Li: College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Shunli Wang: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2022, vol. 15, issue 13, 1-15

Abstract: Accurate online capacity estimation and life prediction of lithium-ion batteries (LIBs) are crucial to large-scale commercial use for electric vehicles. The data-driven method lately has drawn great attention in this field due to efficient machine learning, but it remains an ongoing challenge in the feature extraction related to battery lifespan. Some studies focus on the features only in the battery constant current (CC) charging phase, regardless of the joint impact including the constant voltage (CV) charging phase on the battery aging, which can lead to estimation deviation. In this study, we analyze the features of the CC and CV phases using the optimized incremental capacity (IC) curve, showing the strong relevance between the IC curve in the CC phase as well as charging capacity in the CV phase and battery lifespan. Then, the life prediction model based on automated machine learning (AutoML) is established, which can automatically generate a suitable pipeline with less human intervention, overcoming the problem of redundant model information and high computational cost. The proposed method is verified on NASA’s LIBs cycle life datasets, with the MAE increased by 52.8% and RMSE increased by 48.3% compared to other methods using the same datasets and training method, accomplishing an obvious enhancement in online life prediction with small-scale datasets.

Keywords: lithium-ion battery; incremental capacity; automated machine learning; life prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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