Development of a Hybrid Particle Whale Optimization Algorithm for Electric Vehicle Battery Thermal Runaway Prediction

Accurate prediction of battery thermal runaway (TR) is a critical requirement for electric vehicle (EV) battery management systems (BMSs), as TR remains one of the most severe failure modes in lithium-ion batteries. Conventional neural network training methods may suffer from local optimum entrapment, slow convergence, and unstable performance when applied to nonlinear battery safety data. To address these limitations, this paper proposes a Hybrid Particle Whale Optimization Algorithm-optimized feedforward neural network (HPWOA-FNN) for continuous TR probability prediction and binary high-risk event classification using multivariate EV charging sensor data. The proposed HPWOA combines the rapid convergence capability of Particle Swarm Optimization (PSO) during the initial exploration phase with the exploitation and refinement capability of the Whale Optimization Algorithm (WOA) during the second phase. A global-best transfer mechanism is introduced at the PSO-WOA phase boundary to preserve the best solution identified during exploration and initialize the WOA leader, thereby improving convergence continuity and reducing premature stagnation. The model is evaluated using a 500-sample EV battery-charging dataset containing 12 electrothermal, electrical, mechanical, and environmental features. The proposed HPWOA-FNN outperforms standalone PSO-, WOA-, and Stochastic Fractal Search Algorithm (SFSA)-optimized FNN models across all regression metrics, achieving MSE = 0.000989, RMSE = 0.031442, MAE = 0.027250, R² = 0.9702, and MAPE = 3.8075%. For binary high-risk event detection, HPWOA-FNN achieves the highest AUC of 0.9817 and the lowest false-negative count, reducing missed high-risk events to 7 compared with 9 for PSO, 12 for WOA, and 17 for SFSA. Feature-importance analysis identifies maximum temperature and internal resistance as the dominant predictors, consistent with established thermal runaway mechanisms. The results demonstrate that HPWOA-FNN provides an accurate, interpretable, and computationally practical framework for EV battery thermal runaway prediction and BMS decision support.