Lithium Ion Battery Health Prediction via Variable Mode Decomposition and Deep Learning Network With Self-Attention Mechanism

Abstract

Battery health prediction is very important for the safety of lithium batteries. Due to the factors such as capacity regeneration and random fluctuation in the use of lithium ion battery, the accuracy and generalization ability are poor when using a single scale feature to predict the health state of lithium ion battery. To solve these problems, we propose a comprehensive prediction method based on variational mode decomposition, integrated particle filter, and long short-term memory network with self-attention mechanism. Firstly, the capacity data of lithium ion battery is decomposed by variational mode decomposition to obtain the residual component which can reflect the global degradation trend of lithium ion battery and intrinsic mode functions component that can reflect the local random fluctuation. Then, the particle filter algorithm is employed to predict the residual component, and the long short-term memory network with self-attention mechanism is proposed to predict the intrinsic mode functions component. Finally, the prediction results of each subcomponent are reconstructed to obtain the final prediction value of lithium ion battery health state. The experimental results show that the prediction method proposed in this article has good prediction accuracy and stability.