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.
作者
王勇
陈万顺
WANG Yong;CHEN Wan-shun(Department of Information Engineering,Wuhu Institute of Technology,Wuhu Anhui 241006,China)
出处
《长春师范大学学报》
2019年第6期15-18,共4页
Journal of Changchun Normal University
基金
安徽省质量工程项目“芜湖职业技术学院江苏新通达汽车智能技术校企合作实践基地”(2017sjjd041)
芜湖职业技术学院校级重点项目“基于交互多模型卡尔曼滤波的动力电池荷电状态测量”(Wzyzrzd201702)
