基于一致性估计的车用动力蓄电池组SOC修正法

An Adaptive Method for Automotive Traction Battery Pack SOC Estimation Based on In pack Cell Uniformity Condition

摘要:

目前车用动力蓄电池组荷电状态(SOC)的估计方法在应用时都将电池组看作一个整体，而忽略了组中单体电池之间的差异对整组SOC估计的影响.提出一种基于单体电池一致性估计的车用动力蓄电池组SOC修正方法.此方法采用了自适应神经模糊推理系统的基本原理，通过对模糊逻辑规则库的离线自适应训练，构建了可用于车载电池管理系统（BMS）的SOC一致性模糊推理系统.通过仿真或者试验验证表明，该方法能够在电池组SOC一致性发生变化的情况下，作出较为准确的判断并结合传统的整组SOC估计结果进行修正.说明通过该方法建立的模糊模型经过神经网络自适应学习后具有较好的泛化能力.

Abstract:

The current state of charge (SOC) prediction methods for the traction battery pack (TBP) do not take into consideration of the cell uniformity problem which can not be neglected in TBP consisting of dozens or thousands of battery cells with their own characteristics. A new approach for online TBP SOC adjustment is proposed, which combines the tranditional and adaptive network based fuzzy inference system (ANFIS) methods. Fuzzy inference system (FIS) is used to adjust the traditional SOC estimation results in the pack in running time. Since the ANFIS is introduced, the training stage of the FIS can be completed offline; the trained knowledge base is appropriate for online application in an embedded system with acceptable computation complexity. The model structure, training method and verification process are introduced, and the verification result shows good generalization ability of the trained FIS.