首页|A comparative study of data-driven battery capacity estimation based on partial charging curves

A comparative study of data-driven battery capacity estimation based on partial charging curves

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With its generality and practicality,the combination of partial charging curves and machine learning(ML)for battery capacity estimation has attracted widespread attention.However,a clear classification,fair comparison,and performance rationalization of these methods are lacking,due to the scattered exist-ing studies.To address these issues,we develop 20 capacity estimation methods from three perspectives:charging sequence construction,input forms,and ML models.22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance.Through comprehensive and unbiased comparison,the long short-term memory(LSTM)based neural network exhibits the best accuracy and robustness.Across all 6503 tested samples,the mean absolute percentage error(MAPE)for capacity estimation using LSTM is 0.61％,with a maximum error of only 3.94％.Even with the addition of 3 mV voltage noise or the extension of sampling intervals to 60 s,the average MAPE remains below 2％.Furthermore,the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application.Recommendations for using other competitive methods are also presented.This work provides valuable insights and guidance for estimat-ing battery capacity based on partial charging curves.

Lithium-ion batteryPartial charging curvesCapacity estimationData-drivenSampling frequency

Chuanping Lin、Jun Xu、Delong Jiang、Jiayang Hou、Ying Liang、Xianggong Zhang、Enhu Li、Xuesong Mei

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State Key Laboratory for Manufacturing Systems Engineering,Xi'an Jiaotong University,Xi'an 710049,Shaanxi,China

Shaanxi Key Laboratory of Intelligent Robots,School of Mechanical Engineering,Xi'an Jiaotong University,Xi'an 710049,Shaanxi,China

Department of Electrical Engineering and Automation,Luoyang Institute of Science and Technology,Luoyang 471023,Henan,China

Wuhan Institute of Marine Electric Propulsion,China State Shipbuilding Corporation Limited,Wuhan 430064,Hubei,China

Gresgying Digital Technology Ltd.,Xi'an 710086,Shaanxi,China

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National Natural Science Foundation of ChinaNational Key Research and Development Program of China

520754202020YFB1708400

2024

10.1016/j.jechem.2023.09.025

能源化学
中国科学院大连化学物理研究所 中国科学院成都有机化学研究所

能源化学

CSTPCDEI
影响因子：0.654
ISSN：2095-4956
年,卷(期)：2024.88(1)
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