Improved LSTM-Based Battery SOH Estimation with Differential Evolution Hyperparameter Optimization

Reliable estimation of battery State of Health (SoH) is essential for the safety and longevity of lithium-ion battery systems. Previous work introduced a PSO-aided LSTM (PA-LSTM) model for SoH prediction using NASA’s battery degradation dataset. Building on this, our earlier GA-LSTM model utilized a Genetic Algorithm (GA) for LSTM hyperparameter tuning, achieving RMSE reductions of 12.4% to 76.79% using 70% of the discharge cycle data.In this study, we propose a novel approach for SoH prediction by integrating Long Short-Term Memory (LSTM) networks with Differential Evolution (DE), a more efficient and scalable metaheuristic optimizer. Leveraging DE’s robust convergence behavior and global search capability, the proposed DE-LSTM model is evaluated on the same NASA dataset. Experimental results demonstrate that DE-LSTM outperforms our previous GA-LSTM model, achieving further RMSE reductions and highlighting DE’s effectiveness for hyperparameter optimization in data-driven battery health prognostics.