基于在线参数辨识和改进2RC-PNGV模型的锂离子电池建模与SOC估算研究

doi:10.19799/j.cnki.2095-4239.2021.0220

储能科学与技术 ›› 2021, Vol. 10 ›› Issue (6): 2312-2317.doi: 10.19799/j.cnki.2095-4239.2021.0220

基于在线参数辨识和改进2RC-PNGV模型的锂离子电池建模与SOC估算研究

刘雨洋(), 王顺利(), 谢滟馨, 吉伟康, 张一兴

西南科技大学信息工程学院，四川绵阳 621010

收稿日期:2021-05-19 修回日期:2021-06-10 出版日期:2021-11-05 发布日期:2021-11-03
作者简介:刘雨洋（1997—），男，硕士研究生，研究方向为新能源测控，E-mail：947536218@qq.com|王顺利，教授，研究方向为信号检测与估计、抗干扰处理、控制策略、人工智能和智能计算研究等，E-mail：497420789@qq.com。

Research on Li-ion battery modeling and SOC estimation based on online parameter identification and improved 2RC-PNGV model

Yuyang LIU(), Shunli WANG(), Yanxin XIE, Weikang JI, Yixing ZHANG

School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, China

Received:2021-05-19 Revised:2021-06-10 Online:2021-11-05 Published:2021-11-03

摘要/Abstract

摘要：

锂电池荷电状态（state of charge，SOC）的准确估计对电池安全监测与能量的高效利用具有重要意义。提出一种新的验证模型，首先对电池新一代汽车合作伙伴（PNGV）模型进行改进，考虑电池充放电的差异，加入了二极管电阻的并联网络来代替传统PNGV模型的内阻，在此基础上，增加了一个RC的并联网络来表征电池的动静态特性。以三元锂电池为研究对象，通过遗忘因子最小二乘法（forgetting factor recursive least square，FFRLS）对改进模型进行在线参数辨识，并提出了主充电、放电实验对锂电池工作特性进行仿真分析，通过FFRLS-EKF算法在DST工况下对SOC进行估算。实验结果表明，改进的2RC-PNGV模型能够较好地反映锂电池工作特性，HPPC实验的平均电压误差为0.17%，模型具有较高的精度。主充电过程SOC平均估算误差为0.957%，最大估算误差为5.03%；主放电过程SOC平均估算误差为0.807%，最大估算误差为3.38%，表明改进的2RC-PNGV模型与联合估计算法均可用于SOC实际估算。

关键词: 锂离子电池, PNGV模型, 遗忘因子最小二乘法, 在线参数辨识, 扩展卡尔曼滤波

Abstract:

Accurate estimation of lithium-ion (Li-ion) batteries' state of charge (SOC) is significant for battery safety detection and energy-efficient utilization. A new verification model is proposed. Firstly, the battery partnership for a new generation of vehicles (PNGV) model is improved. Considering the difference between battery charging and discharging, the parallel network of diode resistors is adopted to replace the internal resistance of the traditional PNGV model. On this basis, a resistor-capacitor (RC) parallel network is employed to characterize the dynamic and static characteristics of the battery. Using a ternary Li-ion battery as the research object, online parameter identification of the improved model is performed using the forgetting factor recursive least square method. The main charging and discharging experiments were proposed to simulate and analyze the working characteristics of lithium batteries. The FFRLS-EKF algorithm is used to estimate the SOC under the custom DST condition. The experimental results show that the improved 2RC-PNGV model can reflect the operating characteristics of the Li-ion battery well. The average voltage error of the HPPC experiment is 0.17%, the model has higher accuracy. The average error of SOC estimation in the main charging process is 0.957%, and the maximum estimation error is 5.03%. In the main discharge process, the average error of SOC estimation is 0.807%, with a maximum estimation error of 3.38%. It is demonstrated that both the improved 2RC-PNGV model and the joint estimation algorithm can estimate SOC.

Key words: Li-ion battery, PNGV model, forgetting factor recursive least square, online parameter identification, EKF

中图分类号:

TM 911

刘雨洋, 王顺利, 谢滟馨, 吉伟康, 张一兴. 基于在线参数辨识和改进2RC-PNGV模型的锂离子电池建模与SOC估算研究[J]. 储能科学与技术, 2021, 10(6): 2312-2317.

Yuyang LIU, Shunli WANG, Yanxin XIE, Weikang JI, Yixing ZHANG. Research on Li-ion battery modeling and SOC estimation based on online parameter identification and improved 2RC-PNGV model[J]. Energy Storage Science and Technology, 2021, 10(6): 2312-2317.

图/表 4

参考文献 12

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基于在线参数辨识和改进2RC-PNGV模型的锂离子电池建模与SOC估算研究

Research on Li-ion battery modeling and SOC estimation based on online parameter identification and improved 2RC-PNGV model

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