基于内环修正CDKF算法的锂电池SOC估计OA北大核心CSTPCD
SOC estimation of lithium battery based on inner loop correction cen-tral difference Kalman filter algorithm
针对现有中心差分卡尔曼滤波算法(CDKF)每次滤波仅进行一次状态更新,无法充分发挥其修正作用的问题,提出一种基于内环修正的中心差分卡尔曼滤波算法(ILCDKF).采用带遗忘因子的递推最小二乘法(FFRLS)辨识二阶RC等效电路模型的参数;在CDKF算法的量测更新阶段,依据状态误差协方差矩阵建立状态修正机制,通过内环状态修正以提高滤波精度;通过美国联邦城市运行工况(FUDS)和动态应力测试工况(DST)下的仿真实验来验证所提算法的有效性,结果表明,所提算法与扩展卡尔曼滤波算法(EKF)、无迹卡尔曼滤波算法(UKF)及CDKF相比,在荷电状态(SOC)估计精度和收敛速度方面均有所提升.
Aiming at the problem that the existing central difference Kalman filter(CDKF)algorithm only performs one state update per filter and cannot fully utilize its correction effect,a central differ-ence Kalman filter algorithm based on inner loop correction(ILCDKF)was proposed.The recursive least squares method with forgetting factor(FFRLS)was used to identify the parameters of the second-order RC equivalent circuit model.In the measurement and update stage of the CDKF algo-rithm,a state correction mechanism was established based on the state error covariance matrix,and the filtering accuracy was improved through the inner loop state correction.The effectiveness of the proposed algorithm was verified through the simulation experiments under FUDS and DST condi-tions.The results show that the proposed algorithm improves the estimation accuracy and the conver-gence speed of SOC compared with the extended Kalman filter(EKF),unscented Kalman filter(UKF)and CDKF.
张传明;万佑红;肖杨
南京邮电大学自动化学院,江苏南京 210023||南京邮电大学人工智能学院,江苏南京 210023
动力与电气工程
锂电池荷电状态内环修正中心差分卡尔曼滤波
lithium batterystate of chargeinner loop correctioncentral difference Kalman filter
《电源技术》 2024 (006)
1116-1122 / 7
国家自然科学基金(62073172)
评论