基于HHO-LSTM-KAN模型的锂电池寿命预测OA

Accurately predicting the long-term Remaining Useful Life(RUL)of lithium batteries is critical for en-suring the reliability of lithium battery-powerd systems.This study constructs an LSTM-KAN prediction model by in-tegrating the sequential data processing capability of Long Short-Term Memory(LSTM)network with the superior nonlinear fitting ability of Kolmogorov-Arnold Network(KAN).To enhance model performance,the Harris Hawk Optimization(HHO)algorithm is employed to determine its optimal hyperparameters.Using datasets from the Uni-versity of Maryland(batteries CS2_35 and CS2_36)and NASA(battery B0007),features correlated with battery capacity are first extracted from operational data such as voltage,current,and temperature.Features with a Spearman correlation coefficient greater than 0.9 are then selected as model inputs to reduce data complexity.Subsequently,the HHO algorithm optimizes the hyperparameters of the LSTM-KAN model,and the processed data are fed into the optimized model for RUL prediction.Experimental results demonstrate that the proposed HHO-LSTM-KAN model ef-fectively predicts the long-term degradation trend of lithium batteries.Its performance,in terms of mean squared er-ror and the required number of prediction samples,is superior to that of other benchmark battery life prediction models.