新能源汽车锂离子电池组SOC-SOP联合估计算法

doi:10.3969/j.issn.1674-8484.2022.03.020

汽车安全与节能学报 ›› 2022, Vol. 13 ›› Issue (3): 580-589.DOI: 10.3969/j.issn.1674-8484.2022.03.020

• 汽车节能与环保 • 上一篇

新能源汽车锂离子电池组SOC-SOP联合估计算法

谢翌¹(), 江迪生¹, 张扬军², 李伟¹, 杨瑞³, 钱煜平²

1.重庆大学机械与运载工程学院,重庆 400044,中国
2.清华大学,汽车安全与节能国家重点实验室,北京 100084,中国
3.重庆大学能源与动力工程学院,重庆 400044,中国

收稿日期:2021-11-30 修回日期:2022-06-11 出版日期:2022-09-30 发布日期:2022-10-04
作者简介:谢翌(1983—),男(汉),重庆,副教授。E-mail: claudexie@cqu.edu.cn。
基金资助:
汽车安全与节能国家重点实验室开放课题(KF2031);国家自然科学基金联合项目(U1864212)

Joint estimation algorithm of SOC-SOP for lithium-ion battery pack in new energy vehicles

XIE Yi¹(), JIANG Disheng¹, ZHANG Yangjun², LI Wei¹, YANG Rui³, QIAN Yuping²

1. College of Mechanical and Vehicular Engineering, Chongqing University, Chongqing 400044, China
2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
3. School of Energy and Power Engineering, Chongqing University, Chongqing 400044, China

Received:2021-11-30 Revised:2022-06-11 Online:2022-09-30 Published:2022-10-04

摘要/Abstract

摘要：

为了精确地估计新能源汽车锂离子电池组的状态,该文将串并联电池包中并联电池组简化为大单体,基于一阶阻容模型构建了电池包的等效电路模型。基于自适应扩展Kalman滤波(AEKF)和电池系统多约束算法构建了电池包的荷电状态-功率状态（SOC-SOP）双状态联合估计算法。该算法在精确估计SOC的基础上,考虑了电池多约束对其功率状态（SOP）影响,使电池组SOP算法能够精确地预测电池电流峰值,实现电池组精确SOP估计。结果表明：在模拟循环高速公路燃料经济性试验(HWFET)工况下,SOC估计值收敛后的最大绝对误差仅为2.7%;SOP估计值与日本电动车标准（JEVS）实验测试结果相同且平均相对误差小于3.5%。

关键词: 新能源汽车, 锂离子电池组, 自适应扩展Kalman滤波(AEKF), 荷电状态(SOC), 功率状态(SOP)

Abstract:

The parallel-connected submodule in the serial-parallel connected battery pack was considered as the single battery unit to accurately estimate the state of the lithium-ion battery pack of a new energy vehicle, and the first-order resistance-capacitance model was applied to modelling the battery pack. The adaptive extended Karlman filter (AEKF) and the algorithm with multiple constrains were used for the state of charge- state of power (SOC-SOP) estimation algorithm for the battery pack. Based on the accurate estimation of SOC and multiple-constrain algorithm, SOC-SOP joint estimation algorithm precisely predicted the maximum current through the battery pack and realized the accurate SOP estimation. The results show that the maximum absolute error of the state of charge (SOC) estimate after convergence is only 2.7% under the simulated cyclic highway fuel economy test (HWFET); the state of power (SOP) estimate result is the same as that obtained in the Japanese Electric Vehicle Standard (JEVS) experimental test and the average relative error is less than 3.5%.

Key words: electric vehicle, lithium-ion battery pack, adaptive extended Kalman filtering (AEKF), state of charge(SOC), state of power(SOP)

中图分类号:

TM912

谢翌, 江迪生, 张扬军, 李伟, 杨瑞, 钱煜平. 新能源汽车锂离子电池组SOC-SOP联合估计算法[J]. 汽车安全与节能学报, 2022, 13(3): 580-589.

XIE Yi, JIANG Disheng, ZHANG Yangjun, LI Wei, YANG Rui, QIAN Yuping. Joint estimation algorithm of SOC-SOP for lithium-ion battery pack in new energy vehicles[J]. Journal of Automotive Safety and Energy, 2022, 13(3): 580-589.

图/表 20

图1 锂离子电池组结构

图2 电池包的简化

图3 3P模组的等效电路模型

图4 HPPC测试过程

图5 并联电池子模组（3 P 模组）的电气参数随SOC变化规律

图6 电池组SOC-SOP耦合估计算法的流程及输入及输出

图7 SOC估计模块

图8 SOP估计模块

图9 电池组的实验测试

图10 电池组电流及端电压

图11 不同错误初始SOC下的估计结果

算法	SOC₀	MAE / %	RMSE / %
AEKF	0.3	2.96	1.71
AEKF	0.8	2.52	1.38
EKF	0.3	10.2	3.11
EKF	0.8	10.2	3.02

表1 SOC估计结果

图12 电池组SOC和端电压估计值

图13 JEVS测试结果拟合过程

图14 JEVS测试中电池电流与端电压的变化

SOC / %	0.9	0.7	0.5	0.3
放电峰值电流 / A	-2 544.3	-2 128.2	-1 962.6	-1 718.6
充电峰值电流 / A	476.3	1 008.1	1 342.5	1 420.8

表2 不同SOC下充放电峰值电流

参数	设置值
放电截止电压	2.75 V
充电截止电压	4.25 V
SOC上限	0.9
SOC下限	0.2

表3 并联子模组在SOP估计中约束

图15 电池组放电峰值电流估计值与测试值

	SOC	峰值电流/ kA		相对误差
	SOC	JEVS	SOP估计值	%
放电	0.9	-2.544	-2.478	2.69
	0.7	-2.128	-2.903	1.67
	0.5	-1.963	-1.877	4.54
	0.3	-1.719	-1.672	2.79
充电	0.9	476.300	45.100	—
	0.7	1.008	1.071	5.87
	0.5	1.243	1.327	1.16
	0.3	1.421	1.492	4.79

表4 充放电峰值电流估计值比较

图16 电池组估计SOP

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新能源汽车锂离子电池组SOC-SOP联合估计算法

Joint estimation algorithm of SOC-SOP for lithium-ion battery pack in new energy vehicles

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