A Novel Hybrid Approach for Remaining Useful Life (RUL) and Short-Term Capacity Prediction of Batteries

Abstract

Lithium-ion battery management requires an accurate determination of the batteries’ remaining usable lives. A novel hybrid method used for the RUL and short-term capacity prediction of batteries is presented in this manuscript. The proposed technique is combined with a White Shark Optimizer (WSO) with the Multi-kernel Support Vector Machine (MSVM); therefore, it is called the WSO-MSVM technique. The major goal of this WSO-MSVM method is to attain effective future capacities and RUL forecast for the Lithium-Ion Battery (LIB) with reliable management of uncertainty. The hybrid technique solves the difficulty of forecasting the RUL of LIB. This proposal trains the WSO-MSVM model using the LIB data. The trained model is utilized to forecast the capacity and RUL of LIBs. In this proposed system, capacity, current, and voltage are considered in the discharge operation. The proposed technique validates good reliability and prediction accuracy with this suitable mechanism. Compared to the existing techniques like artificial neural network (ANN), ant lion optimizer-optimized artificial neural network, and adaptive network-based fuzzy inference system, the proposed technique shows fewer errors under charge and discharge conditions. In addition, the uncertainty intervals of the proposed WSO-MSVM technique for the predicted RUL may include the actual remaining life of the battery. The proposed technique’s charging error is lower the other exciting methods.