Artificial-Intelligence-Based Energy Management Strategies for Hybrid Electric Vehicles: A Comprehensive Review
The worldwide drive towards low-carbon transportation has made Hybrid Electric Vehicles (HEVs) a crucial component of sustainable mobility, particularly in areas with limited charging infrastructure. The core of HEV efficiency lies in the Energy Management Strategy (EMS), which regulates the energy...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/14/3600 |
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| Summary: | The worldwide drive towards low-carbon transportation has made Hybrid Electric Vehicles (HEVs) a crucial component of sustainable mobility, particularly in areas with limited charging infrastructure. The core of HEV efficiency lies in the Energy Management Strategy (EMS), which regulates the energy distribution between the internal combustion engine and the electric motor. While rule-based and optimization methods have formed the foundation of EMS, their performance constraints under dynamic conditions have prompted researchers to explore artificial intelligence (AI)-based solutions. This paper systematically reviews four main AI-based EMS approaches—the knowledge-driven, data-driven, reinforcement learning, and hybrid methods—highlighting their theoretical foundations, core technologies, and key applications. The integration of AI has led to notable benefits, such as improved fuel efficiency, enhanced emission control, and greater system adaptability. However, several challenges remain, including generalization to diverse driving conditions, constraints in real-time implementation, and concerns related to data-driven interpretability. The review identifies emerging trends in hybrid methods, which combine AI and conventional optimization approaches to create more adaptive and effective HEV energy management systems. The paper concludes with a discussion of future research directions, focusing on safety, system resilience, and the role of AI in autonomous decision-making. |
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| ISSN: | 1996-1073 |