基于双高斯模型的锂电池剩余使用寿命预测方法

Lithium-ion Battery RUL Prediction Method Based on Double Gaussian Model

准确的剩余使用寿命预测对锂离子电池的性能最大化和维护是至关重要的。为了对锂离子电池的剩余使用寿命进行精准预测,提出一种新颖的双高斯模型用于描述锂离子电池老化过程。首先对常用的几种电池容量衰减经验模型进行分析与评价,并提出性能更优的双高斯模型。随后,基于历史容量数据,利用粒子滤波(Particle filter,PF)技术建立双高斯老化模型,同时引入拟合相关系数与均方根误差评估模型。最后,根据实验室的单体电池老化数据和美国国家航空航天局的电池老化数据,进行剩余使用寿命预测试验,以验证所提出的老化模型的有效性。试验结果表明,所提出的老化模型可以准确地预测锂电池剩余使用寿命,与其他模型相比,预测误差得到明显改善。

For the performance maximization and maintenance of lithium-ion batteries,accurate remaining useful life(RUL)predictions are essential.To accurately predict the RUL of lithium-ion batteries,a novel double Gaussian model is proposed to describe the aging process of lithium-ion batteries.Specifically,several popular empirical models for battery capacity degradation are analyzed and evaluated,and a double Gaussian model with better performance is proposed.Afterward,a double Gaussian aging model is established utilizing the particle filter(PF)technique,based on the historical capacity data.The fitted correlation coefficient and root mean square error are also introduced to assess the model.Finally,the RUL prediction experiments are conducted to verify the verification of the proposed aging model based on the battery aging data from the laboratory’s battery cells and the National Aeronautics and Space Administration(NASA)Ames Prognostics Center of Excellence.The experimental results demonstrate that the proposed aging model can predict the RUL accurately,and the prediction error is significantly improved compared to other models.