A hybrid method for prognostics of Lithium-ion batteries capacity considering regeneration phenomena

Fractional modeling and parameter identification of lithium-ion battery

Gated recurrent unit least-squares generative adversarial network for battery cycle life prediction

The remaining useful life (RUL) prediction of Lithium-ion batteries (LIBs) is a crucial element of battery health management. The accurate prediction of RUL enables the maintenance and replacement of batteries with potential safety hazards, which ensures safe and stable battery operation. This paper develops a new method for the RUL prediction of LIBs, which is combined with complete ensemble empirical mode decomposition with adaptive noise (CEEDMAN), whale optimization algorithm (WOA), and support vector regression (SVR). Firstly, the CEEMDAN is employed to perform noise reduction in battery capacity data for prediction accuracy improvement. Then, an SVR model optimized by the WOA is proposed to predict the RUL. Finally, the public battery datasets are selected to validate the performance of the CEEMDAN-WOA-SVR method. The RUL prediction accuracy of the CEEMDAN-WOA-SVR method is better than the WOA-SVR method. In addition, a comparison is made between the proposed method and the existing methods (artificial bee colony algorithm-SVR method, ensemble empirical mode decomposition-gray wolf optimization-SVR method). The results show that the accurate prediction of the proposed method is superior to the two methods.

Guangxu Hong

Papers

An iterative model of the generalized Cauchy process for predicting the remaining useful life of lithium-ion batteries