储能科学与技术 ›› 2024, Vol. 13 ›› Issue (4): 1142-1153.doi: 10.19799/j.cnki.2095-4239.2023.0889

• 电池智能制造、在线监测与原位分析专刊 • 上一篇    下一篇

基于电热耦合效应的锂电池荷电状态与温度状态联合估计

常小兵1,2(), 侯宗尚1,2, 刘连起1,2, 王光1,2, 谢家乐1,2()   

  1. 1.华北电力大学河北省发电过程仿真与优化控制技术创新中心
    2.保定市综合能源系统状态检测与优化调控重点实验室,河北 保定 071003
  • 收稿日期:2023-12-07 修回日期:2023-12-25 出版日期:2024-04-26 发布日期:2024-04-22
  • 通讯作者: 谢家乐 E-mail:220222216031@ncepu.edu.cn;tellerxie@ncepu.edu.cn
  • 作者简介:常小兵(1998—),男,硕士,研究方向为电池热管理,E-mail:220222216031@ncepu.edu.cn
  • 基金资助:
    国家自然科学基金(52207235);河北省自然科学基金(E2023502026)

Joint estimation of the state of charge and temperature of lithium batteries based on the electric thermal coupling effect

Xiaobing CHANG1,2(), Zongshang HOU1,2, Lianqi LIU1,2, Guang WANG1,2, Jiale XIE1,2()   

  1. 1.North China Electric Power University Hebei Province Power Generation Process Simulation and Optimization Control Technology Innovation Center
    2.Key Laboratory of State Detection and Optimization Control of Comprehensive Energy System in Baoding City, Baoding 071003, Hebei, China
  • Received:2023-12-07 Revised:2023-12-25 Online:2024-04-26 Published:2024-04-22
  • Contact: Jiale XIE E-mail:220222216031@ncepu.edu.cn;tellerxie@ncepu.edu.cn

摘要:

准确估计电池的荷电状态(SOC)和内部温度可以提高电池的性能和安全性。其中,电池模型的准确性和估计算法的适用性是关键。为了解决这两个问题,本文建立了圆柱形锂离子电池的多参数电热耦合模型。模型考虑电池SOC与温度变化之间的耦合关系,并且利用改进的熵热系数实验获得电池运行中产生的可逆热与不可逆热,通过可变遗忘因子最小二乘算法(VFFRLS)进行参数辨识,并对比独立的电模型与热模型的SOC与内部温度估计结果,验证了多参数电热耦合模型的准确性,结果证明所提模型相比较于单独的电热模型,估计精度提高了70%以上。最后,设计了一种基于奇异值分解的卡尔曼滤波(SVD-AUKF)算法来同时在线估计SOC和内部温度,并在改进的动态测试(DST)工况下对所提方法进行实验验证。结果表明:所提方法相较于扩展卡尔曼滤波(EKF)与无迹卡尔曼滤波(UKF)算法,能实现更高精度的SOC和温度估计,SOC与内部温度的平均误差分别是5%和0.2 ℃。

关键词: 可逆热, SOC和温度联合估计, 多参数电热耦合模型, SVD-AUKF算法

Abstract:

Accurate estimation of the State of Charge (SOC) and internal temperature of a battery is pivotal for enhancing its performance and safety. The precision of the battery model and the efficacy of the estimation algorithm play critical roles in this context. This paper introduces a multiparameter thermoelectric coupling model for cylindrical lithium-ion batteries, considering the interplay between SOC and temperature fluctuations. It employs an enhanced entropy heat coefficient experiment to determine reversible and irreversible heat generated during battery operation. For parameter identification, the Variable Forgetting Factor Recursive Least Squares algorithm is utilized. The accuracy of the proposed multiparameter electrothermal coupling model is corroborated by comparing the SOC and internal temperature estimation results with those of standalone electrical and thermal models. The findings indicate that our model achieves an improvement in estimation accuracy exceeding 70% over the conventional electric heating model. Furthermore, we developed a Singular Value Decomposition-based Adaptive Unscented Kalman Filter algorithm for real-time joint estimation of SOC and internal temperature, which was experimentally validated under dynamic stress test conditions. Comparative analysis with Extended Kalman Filter and Unscented Kalman Filter algorithms demonstrates the superior accuracy of our method in SOC and temperature estimations, with average errors of 5% and 0.2°C, respectively.

Key words: reversible heat, joint estimation of SOC and temperature, multi parameter electrothermal coupling model, SVD-AUKF algorithm

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