基于数据-模型双驱动的电池组容量在线估计

doi:10.3901/JME.2025.02.210

机械工程学报 ›› 2025, Vol. 61 ›› Issue (2): 210-221.doi: 10.3901/JME.2025.02.210

• 运载工程 • 上一篇

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基于数据-模型双驱动的电池组容量在线估计

张瑛, 康永哲, 段彬, 张承慧, 杜春水

山东大学控制科学与工程学院济南 250061

收稿日期:2024-01-04 修回日期:2024-07-18 发布日期:2025-02-26
作者简介:张瑛，女，1997年出生，博士研究生。主要研究方向为储能电池安全管控、梯次利用关键技术等。E-mail：202120625@mail.sdu.edu.cn;段彬(通信作者)，男，1982年出生，博士，教授，博士研究生导师。主要研究方向为新能源储能系统控制，动力电池测试及管理等。E-mail：duanbin@sdu.edu.cn
基金资助:
国家自然科学基金资助项目(U1964207，62133007，61821004)。

Online Estimation of Battery Pack Capacity Based on Dual Driven Method of Data and Model

ZHANG Ying, KANG Yongzhe, DUAN Bin, ZHANG Chenghui, DU Chunshui

School of Control Science and Engineering, Shandong University, Jinan 250061

Received:2024-01-04 Revised:2024-07-18 Published:2025-02-26

摘要/Abstract

摘要： 准确的电池组容量估计对电动汽车的安全高效运行具有重要意义，电池组的老化会造成电池单体的容量衰减和单体间的不一致性增加，因此在考虑电池不一致性的前提下，实现电池组容量在线准确估计非常重要。针对上述问题，提出一种数据-模型双驱动的电池组容量在线估计方法，首先提取增量容量曲线峰值高度和面积作为老化特征，建立基于混合核相关向量机的单体容量估计模型，平均相对误差为0.72%，方均根误差为0.91%；其次针对电池内阻造成可充入电量损失问题，提出一种基于自适应双卡尔曼滤波算法的可充入电量在线估计方法，在同时考虑单体容量退化和内阻增大的前提下，预测电池组的实际可充入电量，方均根误差仅为0.06 A·h；最后结合不同老化状态下的开路电压-可充入电量/可放出电量曲线，判断最小可充入电量和最小可放出电量特征单体，以估计的电池单体容量为输入，基于自适应双扩展卡尔曼滤波算法实现电量估计，进而实现电池组容量的准确估计，最大估计误差小于2%。

关键词: 电池组容量, 可充入电量, 增量容量曲线, 相关向量机, 自适应双扩展卡尔曼滤波

Abstract: Accurate battery pack capacity estimation is of great significance for the safe and efficient operation of electric vehicles. The aging of the battery pack causes the capacity attenuation of the cells and the increase in the inconsistency between cells. Therefore, it is very important to achieve accurate online estimation of battery pack capacity under the premise of considering cell inconsistency. To this end, a data-model dual-driven battery pack capacity online estimation method is proposed. Firstly, the peak height and area of the incremental capacity curve are extracted as aging characteristics, and a cell capacity estimation model based on hybrid nuclear correlation vector machine is established. The mean relative error is 0.72% and the root mean square error is 0.91%. Secondly, considering the problem of the loss of charging capacity caused by the internal resistance of the cell, an online estimation method of the charging capacity based on the Adaptive Dual Extended Kalman Filter algorithm is proposed. Under the premise of considering the degradation of cell capacity and the increase of internal resistance at the same time, the remaining charging electric quantity of the battery pack is predicted, and the root mean square error is only 0.06 A·h. Finally, combined with the open-circuit voltage-remaining charging electric quantity/discharging electric quantity curve under different aging states, the minimum charging electric quantity and the minimum discharging electric quantity characteristic cell are determined, and the estimated cell capacity is used as the input to estimate the electric quantity based on the ADEKF algorithm. The battery pack capacity value is calculated accurately and its maximum error is less than 2%.

Key words: battery pack capacity, chargeable quantity, incremental capacity, relevance vector machine, adaptive dual extended Kalman filter

中图分类号:

TM912

张瑛, 康永哲, 段彬, 张承慧, 杜春水. 基于数据-模型双驱动的电池组容量在线估计[J]. 机械工程学报, 2025, 61(2): 210-221.

ZHANG Ying, KANG Yongzhe, DUAN Bin, ZHANG Chenghui, DU Chunshui. Online Estimation of Battery Pack Capacity Based on Dual Driven Method of Data and Model[J]. Journal of Mechanical Engineering, 2025, 61(2): 210-221.

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基于数据-模型双驱动的电池组容量在线估计

Online Estimation of Battery Pack Capacity Based on Dual Driven Method of Data and Model

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