基于卡尔曼滤波算法的电池状态估计OA北大核心
Battery State Estimation Based on Kalman Filter Algorithm
为更好地获得锂离子电池荷电状态 SOC(state-of-charge)估计值,选用二阶等效电路模型作为研究对象,针对带有遗忘因子的递推最小二乘法在参数辨识中易受到噪声等环境因素干扰的缺点,提出偏差补偿最小二乘法来实现模型参数的准确辨识,并结合无迹卡尔曼滤波算法对SOC进行估计.针对无迹卡尔曼滤波算法稳定性差等缺点,提出利用权重向量更新滤波算法中的卡尔曼滤波增益.实验结果表明,所提算法估计SOC的总误差可控制在2.7%以内,验证了算法的鲁棒性和有效性.
To obtain the state-of-charge(SOC)estimation value well,a second-order equivalent circuit model is selected as the research object.Aimed at the disadvantage that the recursive least squares method with a forgetting factor is easy to be disturbed by environmental factors such as noises in the parameter identification,a bias compensation recursive least squares method is proposed to realize the accurate identification of model parameters,and the SOC is estimated combined with the unscented Kalman filter algorithm.In view of the disadvantages of the unscented Kalman filter algorithm such as poor stability,the weight vectors are used to update the Kalman filter gain in the filter algorithm.Experimental results show that the total error of the proposed algorithm in estimating SOC was controlled within 2.7%,which verified the robustness and effectiveness of the algorithm.
王语园;安盼龙;惠亮亮
陕西铁路工程职业技术学院铁道动力学院,渭南 714000
动力与电气工程
电池管理系统锂离子电池荷电状态偏差补偿最小二乘法无迹卡尔曼滤波权重向量
Battery management systemlithium-ion batterystate-of-charge(SOC)bias compensation recursive least squares methodunscented Kalman filterweight vector
《电源学报》 2024 (004)
243-250 / 8
陕西省自然科学基础研究计划资助项目(2021JM-542);渭南市重点研发科技计划项目(STYKJ2022-4);陕西省教育厅一般专项科研计划项目(22JK0327);陕西铁路工程职业技术学院电能质量科技创新团队(KJTD202104);陕西铁路工程职业技术学院科研计划项目(2023KYYB-18)This work is supported by Natural Science Basic Research Program of Shaanxi under the grant 2021JM-542;Weinan Science and Technology Plan Project under the grant STYKJ2022-4;General Special Scientific Research Projects of Education Department of Shaanxi Provincial Government under the grant 22JK0327;Power Quality Science and Technology Innovation Team Project of Shaanxi Railway Institute under the grant KJTD202104;Scientific Research Fund Project of Shaanxi Railway Institute under the grant 2023KYYB-18
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