[1]武强,钟勇,黄志荣,等.变温度下EKF和UKF的锂电池SOC估算对比[J].福建工程学院学报,2022,20(06):580-586.[doi:10.3969/j.issn.1672-4348.2022.06.012]
　WU Qiang,ZHONG Yong,HUANG Zhirong,et al.Comparison of SOC estimation of lithium battery by EKF and UKF at variable temperatures[J].Journal of FuJian University of Technology,2022,20(06):580-586.[doi:10.3969/j.issn.1672-4348.2022.06.012]
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变温度下EKF和UKF的锂电池SOC估算对比()
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《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第20卷
期数:
2022年06期
页码:
580-586
栏目:
出版日期:
2022-12-25

文章信息/Info

Title:
Comparison of SOC estimation of lithium battery by EKF and UKF at variable temperatures
作者:
武强; 钟勇; 黄志荣; 杨华山; 李喆
福建省汽车电子驱动技术重点实验室(福建工程学院)
Author(s):
WU Qiang;  ZHONG Yong;  HUANG Zhirong;  YANG Huashan;  LI Zhe
Key Laboratory for Automotive Electronics and Electric Drive of Fujian Province, Fujian University of Technology
关键词:
温度因素; 荷电状态; 扩展卡尔曼滤波; 无迹卡尔曼滤波
Keywords:
temperature factor;  SOC;  EKF;  UKF
分类号:
U469.72
DOI:
10.3969/j.issn.1672-4348.2022.06.012
文献标志码:
A
摘要:
动力电池的荷电状态(State of Charge，SOC)是预估电动汽车剩余有效行驶里程的重要参数之一。为提高锂电池SOC 的估算精度，考虑了温度对锂电池特性的影响。通过实验得到温度对电池容量的关系曲线，以及得到OCV-SOC-T 的函数映射关系，基于二阶RC 等效电路模型，利用带遗忘因子递推最小二乘法(Forgetting Factor Recursive Least Square， FFRLS)对模型进行实时在线参数辨识。在不同温度和工况条件下，采用扩展卡尔曼滤波(Extended Kalman filter，EKF)和无迹卡尔曼滤波( Un-scented Kalman filter， UKF)算法对锂电池的SOC 进行估算并对比验证，结果表明，EKF 在动态压力测试工况(DST)和美国联邦城市运行工况(FUDS) 的均方根误差分别在4.93%和4.69%以内，UKF 在DST 和FUDS 工况下的均方根误差分别在1.47%和1.49%以内。研究结果表明，FFRLS联合EKF和UKF都可以实时估算SOC，且在不同温度和不同工况条件下，UKF算法相较于EKF算法，抗干扰能力更强，估算精度更高，收敛性更好。
Abstract:
The state of charge (SOC) of power battery is one of the important parameters to estimate the remaining effective driving range of electric vehicles. In order to improve the SOC estimation accuracy of lithium battery, the influence of temperature on the characteristics of lithium battery was considered. The relationship curve of temperature to battery capacity was obtained through experiments, and the function mapping relationship of OCV-SOC-T was obtained. Based on the second-order RC equivalent circuit model, forgetting factor recursive least square (FFRLS) was used to identify the real-time parameters of the model. Under different temperatures and working conditions, EKF algorithm and UKF algorithm were used to estimate and verify the SOC of lithium battery. Results show that the root mean square error of EKF in DST and FUDS working conditions was within 4.93% and 5.3%, respectively. The root mean square error of UKF in DST and FUDS conditions is within 1.49% and 1.57%, respectively. Researches show that FFRLS combined with EKF and UKF can estimate SOC in real time, and UKF algorithm has stronger anti-interference ability, higher estimation accuracy and better convergence than EKF algorithm at different temperatures and under different working conditions.

参考文献/References:

[1] 黄凯, 郭永芳, 李志刚. 动力锂离子电池荷电状态估计综述[J]. 电源技术, 2018, 42（9）: 1398-1401.[2] 孙艳艳, 周雪松, 游祥龙, 等. 基于开路电压法的电池荷电状态估算修正[J]. 内燃机与配件, 2019（19）: 225-226.[3] 李昆, 赵理, 赵博阳, 等. 基于频繁项统计的流-安时积分SOC估计方法[J]. 重庆理工大学学报（自然科学）, 2022, 36（3）: 19-27.[4] 刘晓悦, 魏宇册. 优化神经网络的锂电池SOC估算[J]. 机械设计与制造, 2021（11）: 83-86.[5] LIU X J, DAI Y W. Energy storage battery SOC estimate based on improved BP neural network[J]. Journal of Physics: Conference Series, 2022, 2187（1）: 012042.[6] 马永笠. 基于卡尔曼滤波的SOC估算及其电池管理系统研究[D]. 成都: 电子科技大学, 2019.[7] 王文亮, 何锋, 郑永樑, 等. 基于RLS-EKF联合算法的锂电池SOC估算[J]. 电源技术, 2020, 44（10）: 1498-1501, 1505.[8] 徐劲力, 马国庆. 基于UKF的在线锂离子电池SOC估算研究[J]. 电源技术, 2019, 43（10）: 1615-1618, 1644.[9] LUO M J, GUO Y Z, KANG J Q, et al. Ternary-material lithium-ion battery SOC estimation under various ambient temperature[J]. Ionics, 2018, 24（7）: 1907-1917.[10] 彭泳. 基于温度补偿的动力锂电池SOC估算研究[D]. 合肥: 合肥工业大学, 2020.[11] 申江卫. 车载锂电池宽温度全寿命荷电状态估算研究[D].昆明: 昆明理工大学,2021.DOI:10.27200/d.cnki.gkmlu.2021.002099.[12] 王少华. 电动汽车动力锂电池模型参数辨识和状态估计方法研究[D].长春: 吉林大学, 2021.[13] 王君瑞, 单祥, 贾思宁, 等. 基于扩展卡尔曼滤波的蓄电池组SOC估算[J]. 电源技术, 2020, 44（8）: 1168-1172.

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　Deng Qing,Liu Chengwu.Estimation of SOC as the key technology in battery management system[J].Journal of FuJian University of Technology,2017,15(06):519.[doi:10.3969/j.issn.1672-4348.2017.06.003]

更新日期/Last Update: 2022-12-25