State of health prediction of lithium-ion batteries under early partial data based on IWOA-BiLSTM with single feature

The safe and stable operation of electric vehicles relies on fast and accurate predictions of the state of health (SOH) of the battery. To address challenges such as limited availability of extensive battery aging data or data with informative missingness, the novel SOH prediction method based on the improved method whale optimization algorithm (IWOA)-Bi-directional Long Short-Term Memory (BiLSTM) with strong correlated single aging feature is proposed. Firstly, to accurately predict the accelerated degradation process of the battery capacity, the knee-point in the capacity degradation curve is identified as a starting point for SOH prediction by Bacon-Watts model. Next, a small number of early partial aging features of the battery cycle are extracted, such as time of charging or discharging, and various correlation analysis methods are used to select the single feature with the highest correlation with capacity degradation to reduce the computational complexity of multiple feature factors. Finally, BiLSTM model is established to predict battery SOH. In addition, in order to improve the efficiency of the adjustment for hyperparameters, IWOA is proposed to optimize the BiLSTM's hyperparameters. Compared to the traditional Whale Optimization Algorithm (WOA), IWOA has better global search capability, robustness, and efficiency through enhancements in search strategy, mutation operation, adaptive parameter adjustment, and performance optimization. The proposed method is validated using battery datasets from NASA and CALCE. Compared with BiLSTM and WOA-BiLSTM, the simulation results indicate that the MSE of SOH prediction based on IWOA-BiLSTM method mostly remains below 0.05, and index of agreement (IA) basically maintains higher than 99%.