Hybrid Metaheuristic Feature Selection for Automotive Battery Capacity and Health Prediction Models

Feature selection and model tuning are coupled decisions in real-world electric-vehicle battery analysis: the search must balance predictive performance, feature burden, model complexity, and computational demand. This study develops three integrated hybrids—Adaptive Differential Evolution–Particle Swarm Optimisation (Adaptive DE–PSO), Harris Hawks–Grey Wolf Optimisation (HHO–GWO), and Whale–Marine Predators Optimisation (WOA–MPA)—for joint feature selection and tree-ensemble tuning. The empirical cohort comprises 191 capacity-labelled vehicles (M1 = 100, M2 = 83, M3 = 8) derived from the anonymised EVBattery field dataset, with 64 engineered charging predictors and vehicle-disjoint 60%/20%/20% train/validation/test partitions. Every search arm used a population of 20 for 100 iterations (2020 proposals), and all 13 primary algorithms were evaluated across 30 paired runs; nine mechanism ablations were evaluated under the same protocol. The fixed Default ensemble produced the strongest mean held-out capacity result (R2 = 0.697, 95% CI 0.675–0.719; RMSE = 0.852 Ah) and low-capacity-proxy discrimination (AUROC = 0.942, 95% CI 0.928–0.956). Adaptive DE–PSO was the strongest integrated hybrid for capacity (R2 = 0.659), selecting 19.0 features on average, but it remained significantly below Default after a Holm correction (mean difference −0.038; adjusted p = 0.010). For the proxy endpoint, Adaptive DE–PSO achieved AUROC = 0.911, also below Default (adjusted p < 0.001). Thus, the novelty is not a claim of universal hybrid superiority; it is a leakage-safe, equal-budget, multi-objective framework that quantifies when hybridisation exchanges predictive accuracy for sparsity and exposes negative as well as positive mechanism effects. The proxy is cohort-relative and is not a laboratory-validated state-of-health diagnosis.