The Double-Edged Sword of Artificial Intelligence in Battery Management Systems for Vehicles: A Critical Review of Progress, Perils, and the Path to Practical Implementation

Artificial intelligence (AI) is increasingly investigated for battery management systems (BMSs) in electric vehicles, but laboratory accuracy does not establish suitability for vehicle use. This critical review examines state estimation, degradation and remaining useful life prediction, fault diagnosis, and thermal management through the gap between reported performance and deployment. A structured narrative synthesis retains the target, dataset, validation split, and execution context of representative results rather than pooling heterogeneous metrics. The analysis addresses data leakage, distribution shift, uncertain labels, calibration, memory, timing, energy use, explainability, safety, cybersecurity, and model updates. Compact learned models, hybrid observers, physical constraints, and runtime monitoring are discussed as responses to these constraints. An author-proposed evidence classification and deployment ladder distinguishes demonstrated validation and implementation from the additional evidence needed for a vehicle decision; neither framework has been independently validated. Industrial sources provide deployment context but do not validate proprietary algorithms. The review finds that practical value requires a demonstrated improvement over a feasible baseline, transfer within a declared domain, measured implementation costs, and verified limits on decision authority. Research priorities include reproducible benchmarks, independent validation, embedded testing, and monitored operation.