A Multi-Stage Adaptive Method for Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Swarm Intelligence Optimization

Abstract

The accuracy of predicting the remaining useful life of lithium batteries directly affects the safe and reliable use of the supplied equipment. Since the degradation of lithium batteries can easily be influenced by different operating conditions and the regeneration and fluctuation of battery capacity during the use of lithium batteries, it is difficult to construct an accurate prediction model of lithium batteries. Therefore, research into high-precision methods of predicting the remaining useful life has been a popular topic for the whole-life management system of lithium batteries. In this paper, a new hybrid optimization method for predicting the remaining useful life of lithium batteries is proposed. The proposed method incorporates two different swarm intelligence optimization algorithms. Firstly, the whale optimization algorithm is used to optimize the variational mode decomposition (WOAVMD), which can decompose the historical life data into several trend components and non-trend components. Then, the sparrow search algorithm is applied to optimize the long short-term memory neural network (SSALSTM) to predict the non-trend component and the autoregressive integrated moving average model (ARIMA) is used to predict trend components. Finally, the prediction results of each component are integrated to evaluate the remaining useful life of lithium batteries. Results show that better prediction accuracy is obtained in the prediction experiments for several types of batteries in both the NASA and CALCE battery datasets. The generalization ability of the algorithm has also been effectively improved owing to the optimization of parameters of the variational mode decomposition (VMD) and the long short-term memory neural network (LSTM).