适用于嵌入式控制器的电池状态辨识算法
Battery state identification algorithm for embedded controller
马智远 1王勇 1周凯1
作者信息
- 1. 广东电网有限责任公司广州供电局电力试验研究院,广东广州510410
- 折叠
摘要
蓄电池的荷电状态(SOC)估算问题是电池管理系统研究中的核心问题之一,提升SOC估算的准确性与快速性具有重要的工程实践意义.为解决现有全SOC域拟合的卡尔曼滤波(KF)算法在充放电末端存在的估算误差问题,提出了基于一阶RC模型的扩展KF(EKF)分段拟合优化算法,提高了SOC估算的准确性.最后,在MATLAB/Simulink环境下完成了相应的建模与仿真,并设计搭建了锂离子电池充放电实验平台进行了基于所提SOC估算算法的测试,仿真与实验结果验证了所提估算算法的准确性.
Abstract
The estimation of the state of charge(SOC)of the battery is one of the core issues in the research of the battery management system.It is of great engineering practical significance to improve the accuracy and rapidity of the SOC estimation.In order to solve the estimation error problem of the existing full SOC domain fitting Kalman filtering(KF)algorithm at the charge/discharge terminal,an extended KF(EKF)subsection fitting optimization algorithm based on the first-order RC model is proposed,which improves the accuracy of SOC estimation.Finally,the corresponding modeling and simulation are completed in MATLAB/Simulink environment,and a lithium ion battery charging and discharging experimental platform is designed and built for testing based on the proposed SOC estimation algorithm.The simulation and experimental results verify the accuracy of the proposed estimation algorithm.
关键词
蓄电池/等效电路/荷电状态估算/卡尔曼滤波Key words
battery/equivalent circuit/state of charge estimation/Kalman filtering引用本文复制引用
基金项目
南方电网科技项目(080037KK52200013/GZHKJXM20200050)
出版年
2024
NETL
NSTL
万方数据