基于改进指数模型的锂电池容量估计和RUL预测

Modified Exponential Model-based Capacity Estimation and RUL Prediction for Lithium-ion Batteries

针对锂离子电池老化路径复杂性以及传统经验模型无法准确追踪电池容量衰减轨迹的问题,提出一种结合三指数模型和粒子滤波算法的电池容量估计以及RUL预测模型。首先,建立一种描述电池不同老化下容量衰减轨迹的三指数模型;其次,利用粒子滤波算法对模型参数进行估计;最后,利用NASA和CACLE数据对比分析两种传统经验模型。结果显示,所建模型的MAE和RMSE值分别在0.0058和0.0098以内,其预测精度高于其他两种模型,具有更高的准确性和鲁棒性。

In response to the complexity of the aging path of lithium-ion batteries and the inadequacy of conventional empirical models to accurately track the battery capacity decay trajectory,this paper proposes a battery capacity estimation and remaining useful life(RUL)prediction model that combines a three-exponential model and particle filter algorithm.First a three-exponential model capable of describing different aging battery capacity decay trajectories is established.Second the particle filter algorithm is employed to estimate the parameters of the three-exponential model.Finally the predictive results of the proposed model are compared and analyzed with those of two empirical models using NASA and CACLE datasets.Experimental results indicate that the proposed model achieves MAE and RMSE values within 0.0058 and 0.0098,respectively,demonstrating superior predictive utility to the other two empirical models,and hence exhibits higher accuracy and robustness.