The State of Health Prediction of Li-Ion Batteries Based on ISMA-HKELM

Accurately predicting the State of Health (SOH) of lithium-ion batteries (LIBs) is essential to ensure their long-term stable and safe operation. This paper proposes a novel model, the ISMA-HKELM, which is an Improved Slime Mould Algorithm (ISMA)-optimized Hybrid Kernel Extreme Learning Machine (HKELM), designed for high-precision SOH estimation. We first selected the equal voltage rise time and equal voltage drop time as indirect health indicators, and their validity was rigorously confirmed through Pearson and Spearman correlation tests. Subsequently, the ISMA was utilized to effectively tune the key parameters of the HKELM model. Experimental results demonstrate that the ISMA-HKELM model exhibits superior prediction performance across multiple public datasets, achieving an average R2 value of more than 0.99. Furthermore, the model shows significantly lower Mean Absolute Error (MAE), Mean Bias Error (MBE), and Root Mean Square Error (RMSE) compared to other control models. These results fully prove the advancement and validity of the ISMA-HKELM model for LIB SOH estimation.