Lithium Ion Battery Health Prediction via Variable Mode Decomposition and Deep Learning Network With Self-Attention Mechanism - Ge, Yang; Zhang, Fusheng; Ren, Yong (Frontiers in Energy Research, 2022)
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 battery, the accuracy and generalization ability are poor when using a single scale feature to predict the health state of lithium 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 battery is decomposed by variational mode decomposition to obtain the residual component which can reflect the global degradation trend of lithium battery and intrinsic mode functions components 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 components. Finally, the prediction results of each sub component are reconstructed to obtain the final prediction value of lithium battery health state. The experimental results show that the prediction method proposed in this paper has good prediction accuracy and stability.