RUL PREDICTION FOR LITHIUM-ION BATTERIES BASED ON DWD-SVR MODEL

The remaining useful life（RUL）prediction of Lithium-ion batteries is crucial to ensure the safe and stable operation of Lithium-ion battery energy storage devices. Aiming at the problem of low RUL prediction accuracy due to the non-linear and multi-scale characteristics of the capacity degradation characteristics of Lithium-ion batteries，a RUL prediction method for Lithium-ion batteries based on the discrete wavelet decomposition（DWD）and support vector regression（SVR）model is proposed in the paper. First，the global degradation trend，local regeneration and fluctuation components of capacity are extracted using discrete wavelet decomposition，which can reduce the influence of local regeneration and fluctuation phenomena on the machine learning algorithm to predict the capacity degradation process. Then，each decomposition subsequence is reconstructed based on sample entropy，alignment entropy and K-means clustering method to reduce the number of local regeneration and fluctuating subsequences in the decomposition signals and improve the prediction efficiency. Experimental results based on the NASA lithium-ion battery dataset show that the proposed prediction method is able to ensure the accuracy of global degradation trend prediction while responding to fluctuations in a timely manner to improve the RUL prediction accuracy. © 2025 Science Press. All rights reserved.