基于数据挖掘技术的锂离子电池高效状态估计

doi:10.3901/JME.2025.24.223

摘要/Abstract

摘要： 精准、高效的电池状态估计对车载动力电池管理至关重要。基于数据驱动方法不依赖电池内部复杂的反应机理和底层机制，被广泛应用于荷电状态和健康状态估计领域。针对以往方法中大量数据的训练和测试容易导致较高的计算复杂度，提出模糊信息粒化新型数据挖掘技术。首先，提出非对称高斯型隶属度函数，在上边界、均值和下边界三个粒化层面进一步提高数据挖掘的性能。然后，结合高斯过程回归方法并开展锂离子电池循环测试进行荷电状态估计性能验证。最后，建立电池容量估计模型利用公开数据集验证健康状态预测性能。结果表明，结合模糊信息粒化技术后，高斯过程回归方法的荷电状态估计精度在两种电流工况下分别提升15.38%和31.25%，计算时间可从26.9 s降低至3.3 s。模糊信息粒化进行健康状态估计在五种机器学习方法中能提供高精度的同时只需要最低的计算成本，从而为基于数据驱动方法的锂离子电池高效状态估计提供思路和指导。

关键词: 锂离子电池, 模糊信息粒化, 高斯过程回归, 荷电状态, 健康状态

Abstract: Accurate and efficient battery health estimation is very important for vehicle battery management. The data-driven method, which does not depend on the complex reaction mechanism and underlying mechanism inside the battery, is widely used in the field of SOC and state of health estimation. Due to the high computational complexity caused by training and testing a large amount of data in the previous methods, a new data mining technology of fuzzy information granulation is proposed. Firstly, the asymmetric Gaussian membership function is proposed to further improve the performance of data mining at three granular levels: upper boundary, mean and lower boundary. Then, a lithium-ion battery cycle test is conducted in combination with the Gaussian process regression method to verify the performance of the state of charge estimation. Finally, a battery capacity estimation model is established and the performance of the health state prediction is verified using a public dataset. The results show that by combining the fuzzy information granulation technology, the state of charge estimation accuracy of the Gaussian process regression method is improved by 15.38% and 31.25%, respectively, under the two current conditions, and the computation time can be reduced from 26.9 s to 3.3 s. The state of charge estimation by fuzzy information granulation can achieve high accuracy among the five machine learning methods and require only the lowest computational cost, thus providing ideas and guidance for highly efficient state of charge estimation of lithium-ion batteries based on data-driven methods.

Key words: lithium-ion batteries, fuzzy information granulation, Gaussian process regression, state of charge, state of health

中图分类号:

TM912

欧阳天成, 汪成超, 叶今禄, 徐裴行, 张志强, 潘文军, 许恩永. 基于数据挖掘技术的锂离子电池高效状态估计[J]. 机械工程学报, 2025, 61(24): 223-234.

OUYANG Tiancheng, WANG Chengchao, YE Jinlu, XU Peihang, ZHANG Zhiqiang, PAN Wenjun, XU Enyong. Efficient State Estimation of Lithium-ion Battery Based on Data Mining Technology[J]. Journal of Mechanical Engineering, 2025, 61(24): 223-234.

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