Hybrid electric vehicles (HEVs) rely on advanced energy management systems (EMS) to optimallycoordinate power flow between multiple energy sources, thereby enhancing fuel efficiency and reducing emissions.This comprehensive review examines 304 peer-reviewed publications, with an in-depth analysis of 30 highly relevantstudies published between 2009-2024. Four principal EMS categories are identified: optimization-based methods,predictive control strategies, learning-based techniques, and hybrid approaches. Recent findings report fuel economyimprovements of 4.7-13.2% and battery life extensions of up to 54.9%. The analysis reveals a clear transition towardintelligent, adaptive, and real-time control frameworks. In particular, hybrid architectures integrating reinforcementlearning with model predictive control demonstrate strong potential for practical implementation and next-generationvehicle integration.
| Mualliflar | Asretdinova L.J., Gulnora Sh.Y. |
|---|---|
| Jurnal | Муҳандислик ва Иқтисодиёт |
| Nashr sanasi | 2026-02-01 |
| Jild | 4 |
| Son | 2 |
| Til | Ingliz |
Hybrid electric vehicles; Energy management systems; Model predictive control; Reinforcement learning; Optimization methods; Intelligent control; Multi-objective optimization; Real-time implementation.
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