采用GRU-MC混合算法的锂离子电池RUL预测

锂离子电池的剩余使用寿命预测是电池管理系统的重要组成部分.为解决锂离子电池剩余使用寿命预测不准确的问题,提出一种基于门控循环单元和马尔科夫链的锂离子电池剩余使用寿命预测方法.首先,对数据进行数据清洗和数据规范,选择构建特征矩阵;然后,搭建基于门控循环单元网络的预测模型,并运用马尔科夫链算法对预测模型的初步结果进行误差修...

RUL prediction of lithium-ion battery using GRU-MC hybrid algorithm

The remaining useful life (RUL) prediction of lithium-ion batteries is an important part of the battery management system. In order to solve the problem of inaccurate prediction of the remaining useful life of lithium-ion batteries, a method for predicting the remaining useful life of lithium-ion batteries based on gated recurrent unit and Markov chain (GRU-MC) is proposed. Firstly, perform data cleaning and data standardization on the data, and choose to construct a feature matrix. Then, a prediction model based on the gated recurrent unit network is built, and the Markov chain algorithm is used to correct the initial results of the prediction model to obtain the final prediction result. The performance of this method is verified on multiple public datasets, and compared with convolution neural network, long short-term memory network and other algorithms. The results show that this method has excellent performance in predicting the remaining useful life of lithium-ion batteries.