Artificial Intelligence Algorithms for Hybrid Electric Powertrain System Control: A Review

Dawei Zhong, Bolan Liu (), Liang Liu, Wenhao Fan and Jingxian Tang
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Dawei Zhong: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Bolan Liu: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Liang Liu: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Wenhao Fan: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Jingxian Tang: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Energies, 2025, vol. 18, issue 8, 1-30

Abstract: With the accelerating depletion of fossil fuels and growing severity of air pollution, hybrid electric powertrain systems have become a research hotspot in transportation, owing to their ability to improve fuel economy and reduce emissions. However, optimizing the control of these systems is challenging, as it involves multi-power source coordination, dynamic operating condition adaptation, and real-time energy distribution. Traditional control methods, whether rule-based or optimization-based, often lack global optimality and adaptability. In recent years, artificial intelligence algorithms have provided new solutions for the intelligent control of hybrid electric powertrain systems with their powerful nonlinear modeling capabilities, data-driven optimization, and adaptive learning capabilities. This paper systematically reviews the research progress of artificial intelligence algorithms in hybrid electric powertrain systems. First, the architecture classification of hybrid electric powertrain systems is introduced. Secondly, the advantages and disadvantages of rule-based and optimization-based energy management strategies are summarized. Then, the existing research on the application of artificial intelligence algorithms in hybrid electric powertrain systems is systematically reviewed, and the advantages, disadvantages, and specific applications of various algorithms are analyzed in detail. Finally, the future application direction of artificial intelligence algorithms in hybrid electric powertrain systems is prospected.

Keywords: hybrid electric powertrain system; artificial intelligence algorithms; deep learning; reinforcement learning; energy management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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