基于Simulink仿真平台的电动汽车容量融合估计研究

Research on Electric Vehicle Capacity Fusion Estimation Based on Simulink Simulation Platform

针对传统锂离子电池容量估计精度不高且鲁棒性较差等问题,提出了一种基于经验模型与数据驱动反馈校正的容量融合估计方法。利用公开的NASA电池数据集获取锂离子动力电池的容量老化数据,首先采用容量在线辨识的方法对电池容量进行初步估计,在此基础上使用阿伦尼乌斯模型拟合容量衰减曲线。最后采用双卡尔曼滤波分别进行模型参数修正和容量估计。结果表明,本文提出的融合估计方法在实现容量连续预测的同时也提高了容量估计的精度和鲁棒性。适用于电动汽车BMS健康状态估计,在电动汽车领域有较好的应用前景。

A capacity fusion estimation method based on the empirical model and data-driven feedback cor-rection is proposed to address the issues of low accuracy and poor robustness in traditional lithi-um-ion battery capacity estimation. Using the publicly available NASA battery dataset to obtain ca-pacity aging data for lithium-ion power batteries, the initial estimation of battery capacity was first carried out using the online capacity identification method. Based on this, the Arrhenius model was used to fit the capacity decay curve. Finally, dual Kalman filtering is used for model parameter cor-rection and capacity estimation. The results show that the fusion estimation method proposed in this paper not only achieves continuous capacity prediction, but also improves the accuracy and robustness of capacity estimation. Suitable for estimating the health status of electric vehicle BMS, it has good application prospects in the field of electric vehicles.