Remaining Useful Life Prediction for Lithium-Ion Batteries Based on CS-VMD and GRU

Abstract

Accurate prediction the remaining useful life (RUL) and estimation the state of health (SOH) are critical to the management of lithium-ion batteries. In this paper, a lithium battery capacity prediction method based on cuckoo search optimization variational mode decomposition (CS-VMD) and gated recurrent unit (GRU) is proposed. Firstly, the VMD algorithm is used to divide the capacity into some intrinsic mode functions (IMFs) to reduce the impact of capacity regeneration and other situations. The number of decomposition layers and the quadratic penalty factor of VMD are optimized by the CS algorithm. Then, the GRU network is introduced to capture small changes in the capacity degradation process and perform the capacity prediction of decomposed sequence. Finally, some prediction results are integrated effectively. Based on two publicly available lithium-ion battery datasets, the model proposed in this paper can significantly reduce the complexity of the sequence and have high prediction accuracy, which is better than other prediction models. The root mean square error (RMSE) is controlled within 2%, and the maximum mean absolute error (MAE) does not exceed 2%.

Keywords

• Lithium-ion battery RUL prediction
• CS-VMD
• GRU