储能科学与技术 ›› 2024, Vol. 13 ›› Issue (9): 3030-3041.doi: 10.19799/j.cnki.2095-4239.2024.0659

• AI辅助先进电池设计与应用专刊 • 上一篇    下一篇

基于电热耦合模型的宽温域锂离子电池SOC/SOP联合估计

刘莹1,2(), 孙丙香1,2(), 赵鑫泽1,2, 张珺玮1,2   

  1. 1.北京交通大学国家能源主动配电网技术研发中心，北京 100044
    2.北京交通大学载运装备多源动力系统教育部重点实验室，北京 100044
  • 收稿日期:2024-07-16 修回日期:2024-08-01 出版日期:2024-09-28 发布日期:2024-09-20
  • 通讯作者: 孙丙香 E-mail:22121479@bjtu.edu.cn;bxsun@bjtu.edu.cn
  • 作者简介:刘莹（1999—），女，硕士研究生，研究方向为锂离子电池状态估计，E-mail：22121479@bjtu.edu.cn；

Joint estimation of SOC/SOP for lithium-ion batteries across a wide temperature range using an electro-thermal coupling model

Ying LIU1,2(), Bingxiang SUN1,2(), Xinze ZHAO1,2, Junwei ZHANG1,2   

  1. 1.National Active Distribution Network Technology Research Center(NANTEC), Beijing Jiaotong University, Beijing 100044, China
    2.Key Lab. of Vehicular Multi-Energy Drive Systems (VMEDS), Ministry of Education, Beijing Jiaotong University, Beijing 100044, China
  • Received:2024-07-16 Revised:2024-08-01 Online:2024-09-28 Published:2024-09-20
  • Contact: Bingxiang SUN E-mail:22121479@bjtu.edu.cn;bxsun@bjtu.edu.cn

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摘要：

准确的状态估计对于锂离子电池安全可靠运行具有重要意义，但由于非线性强，多参数耦合，实现宽温域多参数联合在线估计难度较大。考虑到温度影响，建立电热耦合模型，采用扩展卡尔曼滤波算法(EKF)在线辨识电池参数，通过电压及温度仿真验证了模型的准确性；然后针对无迹卡尔曼滤波算法(UKF)历史数据利用率低的问题，引入多新息理论(MI)改进UKF，改进后的算法在非电压平台区荷电状态(SOC)估计均方根误差不超过1.2%，相较于改进前误差降低了30%以上，并结合安时积分法设计切换算法，解决了MIUKF算法在磷酸铁锂电池电压平台区无法通过电压反馈修正SOC估计误差的问题，实现了宽温域复杂工况下全区间SOC的准确估计，在不同SOC初始值条件下验证了结合算法的准确性，均方根误差不超过3%，为峰值功率(SOP)估计提供了可靠的SOC值；最后将温度约束引入到SOP估计中，提出多约束条件下的SOP估计方法，结果表明在高温条件下，温度起到关键限制作用，可以防止电池温升过大，减少安全隐患。

关键词: 磷酸铁锂电池, 宽温域, SOC/SOP联合估计, 电热耦合模型, 改进UKF, 多约束条件

Abstract:

Accurate state estimation is crucial for ensuring the safe and reliable operation of lithium-ion batteries. However, achieving simultaneous online estimation of multiple parameters across a broad temperature range is challenging due to strong nonlinearity and multi-parameter coupling. To address this, an electro-thermal coupling model was developed, and battery parameters were identified online using the extended Kalman filter algorithm, the model's accuracy was verified by voltage and temperature simulations. To enhance the utilization of historical data and address the limitations of unscented Kalman filter algorithm (UKF), the multi-innovation theory (MI) was introduced to improve the UKF. The root mean square error of state of charge (SOC) estimation with the improved algorithm in the non-voltage platform areas is reduced to under 1.2%, representing more than 30% improvement. A switching algorithm was also designed, integrating the ampere-hour method to overcome the MIUKF algorithm's limitation of not correcting SOC estimation errors through voltage feedback in the voltage platform areas of lithium iron phosphate batteries. This approach enabled accurate full-range SOC estimation under complex working conditions and at various temperatures. The combined algorithm's accuracy was validated across different initial SOC values, with a root mean square error of less than 3%, providing a reliable SOC value for state of power (SOP) estimation. Finally, under multiple constraint method, a temperature constraint was introduced in SOP estimation. The results show that at high temperature,temperature limitation plays a critical role in preventing excessive temperature rise, thereby reducing potential safety hazards.

Key words: lithium iron phosphate battery, wide temperature range, SOC/SOP joint estimation, electro-thermal coupling model, improving UKF, multiple constraints

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