基于多模型模糊扩展卡尔曼滤波的电池SOC预测
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摘要
随着电池充放电次数的增加,电池充放电特性会发生变化,难以使用单一数学模型加以描述,从而限制了传统卡尔曼滤波预测电池SOC值的精度。针对不同充放电次数的电池分别建立数学模型,并通过卡尔曼滤波对不同模型的电池荷电状态进行预测。采用模糊推理确定不同模型预测结果的权重,并加权求和作为最终结果。实验表明,该预测方法在不增加测量装置硬件成本的基础上,有效提高了电池SOC值的预测精度。
As the increasing of the times of battery charge and discharge, battery charge and discharge characteristics will change, which is difficult to be described by a single mathematical model. The accuracy of the conventional Kalman filter to predict the SOC value is limited by the accuracy of mathematical model.Different mathematical models were established for batteries with different charge and discharge times. Then different predictions were conducted for different models. Fuzzy inference was used to determine the weight of the prediction results of different models, and the weighted summation is used as the final result. Experiments show that the prediction method can effectively improve the prediction accuracy of battery SOC value without increasing the hardware cost of the measurement device.
As the increasing of the times of battery charge and discharge, battery charge and discharge characteristics will change, which is difficult to be described by a single mathematical model. The accuracy of the conventional Kalman filter to predict the SOC value is limited by the accuracy of mathematical model.Different mathematical models were established for batteries with different charge and discharge times. Then different predictions were conducted for different models. Fuzzy inference was used to determine the weight of the prediction results of different models, and the weighted summation is used as the final result. Experiments show that the prediction method can effectively improve the prediction accuracy of battery SOC value without increasing the hardware cost of the measurement device.
引文
[1]徐研科,范兴明,张鑫,等.基于EKF算法的锂电池SOC评估方法[J].桂林电子科技大学学报,2018(3):189-193.
[2]皮钒,王耀南,孟步敏.基于扩展PSO和离散PI观测器的电池SOC估计[J].电子测量与仪器学报,2016(1):11-19.
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[4]郭向伟,华显,付子义,等.模型参数优化的卡尔曼滤波SOC估计[J].电子测量与仪器学报,2018(18):186-192.
[5]颜湘武,郭玉威,王雨薇,等.基于GNL模型自适应无迹卡尔曼滤波的电动汽车荷电状态估计[J].科学技术与工程,2018(30):94-100.
[6]胡新宇,张锋,王博思,等.基于EKF的18650锂电池SOC在线估算[J].激光杂志,2016(5):72-75.
[7]唐帅帅,高迪驹.基于自适应卡尔曼滤波的磷酸铁锂电池荷电状态估计研究[J].电子测量技术,2018(14):1-5.
[2]皮钒,王耀南,孟步敏.基于扩展PSO和离散PI观测器的电池SOC估计[J].电子测量与仪器学报,2016(1):11-19.
[3]田野,宋凯.考虑电池特性的多模型Kalman滤波SOC估计[J].汽车安全与节能学报,2018(2):223-230.
[4]郭向伟,华显,付子义,等.模型参数优化的卡尔曼滤波SOC估计[J].电子测量与仪器学报,2018(18):186-192.
[5]颜湘武,郭玉威,王雨薇,等.基于GNL模型自适应无迹卡尔曼滤波的电动汽车荷电状态估计[J].科学技术与工程,2018(30):94-100.
[6]胡新宇,张锋,王博思,等.基于EKF的18650锂电池SOC在线估算[J].激光杂志,2016(5):72-75.
[7]唐帅帅,高迪驹.基于自适应卡尔曼滤波的磷酸铁锂电池荷电状态估计研究[J].电子测量技术,2018(14):1-5.