基于云端数据充电初期片段的电池极化参数辨识

doi:10.3969/j.issn.1674-8484.2025.02.013

汽车安全与节能学报 ›› 2025, Vol. 16 ›› Issue (2): 294-302.DOI: 10.3969/j.issn.1674-8484.2025.02.013

基于云端数据充电初期片段的电池极化参数辨识

王丽梅¹(), 崔艳伟¹, 孙景景¹, 赵秀亮²^,^*(), 刘良¹, 盘朝奉¹

1.江苏大学汽车工程研究院，镇江 212013，中国
2.江苏大学汽车与交通工程学院，镇江 212013，中国

收稿日期:2024-09-29 修回日期:2024-12-12 出版日期:2025-04-30 发布日期:2025-04-22
通讯作者: * 赵秀亮，副教授。E-mail：zhaoxl@ujs.edu.cn。
作者简介:王丽梅（1988—），女（汉），江苏，教授。E-mail：wanglimei@ujs.edu.cn。
基金资助:
国家自然科学基金项目(52477214);国家自然科学基金项目(52072155);智能绿色车辆与交通全国重点实验室开放基金课题(KFY2401)

Identification of battery polarization parameters based on initial charging segment of cloud data

WANG Limei¹(), CUI Yanwei¹, SUN Jingjing¹, ZHAO Xiuliang²^,^*(), LIU Liang¹, PAN Chaofeng¹

1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
2. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China

Received:2024-09-29 Revised:2024-12-12 Online:2025-04-30 Published:2025-04-22

摘要/Abstract

摘要：

为了提高电池极化参数在线辨识的精度及速度，提出了一种基于云端数据的基准极化参数辨识方法。通过开展电池充放脉冲实验，研究电池极化参数特性；基于云端数据充电初期片段，采用类比混合脉冲功率性能（HPPC）方法，获取充电极化参数；以充电极化参数为约束，利用变遗忘因子递推最小二乘法（VFFRLS），计算了放电极化参数。结果表明：本文方法的电池时间常数范围为34~53 s，在云端相应小电流倍率下极化参数不随倍率变化；充电极化内阻和极化电容的计算结果与实验结果吻合；添加约束后的在线辨识方法的收敛速度，与未添加约束相比，最少提高了6%。

关键词: 电池充电放电, 极化参数, 云端数据, 离线辨识, 类比混合脉冲功率性能（HPPC）法, 变遗忘因子递推最小二乘法（VFFRLS）

Abstract:

A benchmark polarization parameter identification method was proposed based on cloud data to enhance the accuracy and the speed of online identification of battery polarization parameters. The characteristics of battery polarization parameters were investigated by conducting charge-discharge pulse experiments. A method analogous was employed by utilizing the initial charging segment from cloud data through the Hybrid Pulse Power Characterization (HPPC) tests to obtain the charging polarization parameters. The Variable Forgetting Factor Recursive Least Squares (VFFRLS) algorithm was applied with the identified charging polarization parameters as constraints to compute the discharging polarization parameters. The results indicated that this method yielded battery time constants ranging from 34~53 s, and the polarization parameters remained invariant with respect to the current rate under corresponding low current rates in the cloud environment. The calculated charging polarization resistance and polarization capacitance aligned well with laboratory results. The convergence speed of the proposed constrained online identification method was improved by at least 6% compared with the unconstrained identification method.

Key words: battery charging and discharging, polarization parameter, cloud data, off-line identification, hybrid pulse power characterization (HPPC) analogy, variable forgetting factor recursive least square (VFFRLS) algorithm

中图分类号:

TM912

王丽梅, 崔艳伟, 孙景景, 赵秀亮, 刘良, 盘朝奉. 基于云端数据充电初期片段的电池极化参数辨识[J]. 汽车安全与节能学报, 2025, 16(2): 294-302.

WANG Limei, CUI Yanwei, SUN Jingjing, ZHAO Xiuliang, LIU Liang, PAN Chaofeng. Identification of battery polarization parameters based on initial charging segment of cloud data[J]. Journal of Automotive Safety and Energy, 2025, 16(2): 294-302.

图/表 14

电池单体的标称电压	3.65 V
电池单体的工作电压	2.5~4.2 V
电池单体的额定容量	29 Ah
电池包的标称电压	332 V
电池包额的定容量	145 Ah

表1 单体电池与动力电池包参数

图1 一个完整充电片段下单体电池平均电流变化

图2 单体电池平均充电电流分布和倍率次数统计

图3 基于云端数据的放电倍率分布图

图4 倍率充放电脉冲工况电压、电流变化

图5 一阶RC等效电路模型原理图

图6 不同时间域拟合的时间常数

图7 不同SOC下极化参数随倍率变化趋势

图8 充电片段初期单体电池平均电流、电压

图9 片段1前60 s的电压、电流

图10 基于充电片段初期计算的极化参数

图11 放电片段1 (23~27 ℃)在线辨识极化参数结果

图12 放电片段2 (21~24 ℃)在线辨识极化参数结果

图13 放电片段3 (24~31 ℃)在线辨识极化参数结果

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基于云端数据充电初期片段的电池极化参数辨识

Identification of battery polarization parameters based on initial charging segment of cloud data

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