Multi-Objective Feature Selection Using HPWOA for Improved BMS Fault Diagnosis in Electric Vehicles

Abstract

Battery management systems (BMSs) in electric vehicles (EVs) are instrumented with an increasing number of heterogeneous sensors, many of which contribute redundant or noisy measurements that increase computational cost without improving diagnostic accuracy. This paper proposes a Binary Hybrid Particle Whale Optimization Algorithm (BHPWOA) for multi-objective feature selection targeting three-class BMS fault diagnosis: OK, Warning, and Critical. The method is evaluated using an 18-feature EV charging dataset with n=500 samples. BHPWOA encodes candidate feature subsets as binary masks in a continuous [0,1] position space. It executes a Binary Particle Swarm Optimization (BPSO) phase during the first 50 iterations to rapidly identify a promising subset region, then transfers the global-best mask as the Whale Optimization Algorithm (WOA) leader for the remaining 50 iterations of bubble-net exploitation. A multi-objective fitness function simultaneously penalises classifier error and subset size, directly optimising the accuracy–cost trade-off. BHPWOA selects four features out of 18, corresponding to a 77.8% reduction, and achieves accuracy =0.710 and macro-F1 =0.4455 on the held-out test set. It outperforms all-feature KNN F10.2997, standalone BPSO with six selected features F10.4603, BWOA with two selected features F10.4026, and BSFSA with five selected features F10.4216 on the Pareto-dominant combined fitness objective. The selected subset CellVoltageVChargeCurrentASOC%ChargePowerkW achieves the best fitness score of −0.5555, enabling a 77.8% sensor-cost reduction while improving fault detection. Stability analysis across five independent random seeds confirms a mean feature count of 4.0±0.7 and a mean macro-F1 of 0.441±0.021, demonstrating algorithmic robustness.