基于温变RC模型与SMFEKF算法的电池SOC估计
Battery SOC Estimation Based on Temperature-dependent RC Model and SMFEKF Algorithm
程贤福 1李晓静 1刘霏霏 1曾建邦1
作者信息
- 1. 华东交通大学 载运工具与装备教育部重点实验室,江西 南昌 330013
- 折叠
摘要
针对SOC估计精度以及鲁棒性的要求,以锂离子电池单体为研究对象,考虑温度变化对开路电压、极化电阻、极化电容以及容量的影响,建立了温变二阶RC等效电路模型,仿真结果表明该模型比二阶RC模型具有更高的精度.基于该模型,采用了多重次优渐消因子的扩展卡尔曼滤波算法进行SOC估计,结果表明,在变温工况下,相比EKF算法,基于变温模型和恒温模型的SMFEKF算法的SOC估计均方根误差分别减少了42.7%和48.2%,能够保证估算结果有较强的鲁棒性.在变温环境DST工况下,基于变温模型的SOC估计结果最大相对误差和均方根误差均小于恒温模型,证明该模型的温度适应性较强,在变温条件下能有较高的估计精度.
Abstract
To meet the requirements of SOC estimation accuracy and robustness,a temperature-dependent second-order RC equivalent circuit model is established,taking a lithium-ion battery cell as the study object and considering the effects of temperature change on open-circuit voltage,polarization resistance,polarization capacitance,and capacity.Simulation results show that the model has higher accuracy than the second-order RC model.Based on this model,the extended Kalman filter algorithm with multiple suboptimal fading factor is used to estimate SOC.The results show that the root mean square error of SOC estimation of the SMFEKF algorithm based on the variable temperature model and the constant temperature model is reduced by 42.7%and 48.2%,respectively,compared with the EKF algorithm under variable temperature conditions,which can ensure strong robustness of estimation results.The maximum relative error and root mean square error of SOC estimation results based on the variable temperature model are smaller than those of the constant temperature model under the DST condition in the variable temperature environment,which proves that the model has strong temperature adaptability and can have higher estimation accuracy under the variable temperature condition.
关键词
锂电池/荷电状态/次优渐消因子/温变等效电路模型/SMFEKF算法Key words
lithium battery/state of charge/suboptimal fading factor/temperature-dependent equivalent circuit model/SMFEKF algorithm引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据