Optimal energy management strategies for hybrid electric vehicles - A recent survey of machine learning approaches

Aug 2, 2026

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Hybrid Electric Vehicles (HEVs) reduce pollution and improve fuel savings, but their benefit depends on energy management strategies (EMSs) that directly shape power allocation between engine and motor. Achieving both fuel economy and strong vehicle performance is challenging because EMSs must handle nonlinear behaviors and diverse operating conditions. The article reviews representative EMS approaches from the literature and analyzes a growing shift toward machine learning and AI techniques. The findings highlight how learning-based algorithms can better model complex HEV patterns and adapt to dynamic environments to improve EMS effectiveness.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Role and objectives of energy management strategies

Driving-cycle based control focus

Existing EMS approaches: rule-based, optimal control, reinforcement learning

Journal of Engineering Research xxx (xxxx) xxx Contents lists available at ScienceDirect Journal of Engineering Research journal [homepage: www.journals.elsevier.com/journal-of-engineering-research](homepage: www.journals.elsevier.com/journal-of-engineering-research) | Optimal energy management strategies for hybrid electric vehicles: A recent survey of machine learning approaches
Julakha Jahan Juia, Mohd Ashraf Ahmad a, *, M.M. Imran Mollab, Muhammad Ikram Mohd Rashida
a Faculty of Electrical and Electronics Engineering Technology (FTKEE), Universiti Malaysia Pahang (UMP), Pekan, Pahang, Malaysia b Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Hybrid Electric Vehicles Machine Learning
Energy Management Strategies | | Hybrid Electric Vehicles (HEVs) have emerged as a viable option for reducing pollution and attaining fuel savingsin addition to reducing emissions. The effectiveness of HEVs heavily relies on the energy management strategies (EMSs) employed, as it directly impacts vehicle fuel consumption. Developing suitable EMSs for HEVs poses a challenge, as the goal is to maximize fuel economy yet optimize vehicle performance. EMSs algorithms are critical in determining power distribution between the engine and motor in HEVs. Traditionally, EMSs for HEVs have been developed based on optimal control theory. However, in recent years, a rising number of people have been interested in utilizing machine-learning techniques to enhance EMSs performance. This article presents a current analysis of various EMSs proposed in the literature. It highlights the shift towards integrating machine learning and artificial intelligence (AI) breakthroughs in EMSs development. The study examines numerous case studies, and research works employing machine learning techniques across different categories to develop energy management strategies for HEVs. By leveraging advancements in machine learning and AI, researchers have explored innovative approaches to optimize HEVs’ performance and fuel economy. Key conclusions from our investigation show that machine learning has made a substantial contribution to solving the complex problems associated with HEV energy management. We emphasize how machine learning algorithms may be adjusted to dynamic operating environments, how well they can identify intricate patterns in hybrid electric vehicle systems, and how well they can manage non-linear behaviors. | Introduction In response to rising concerns about global warming and climate change, vehicle emission regulations are becoming increasingly stringent [1]. This has led to significant advancements in vehicle electrification and hybridization to comply with these regulations. One of the most impactful strategies to meet the rigorous emissions standards is the substitution of traditional vehicles powered by internal combustion with HEVs [2]. Hybrid electric vehicles (HEVs) combine an electric motor driven by a rechargeable battery and a traditional internal combustion engine to provide a cutting-edge method of transportation propulsion. Because of this hybridization, HEVs may smoothly transition between using an electric motor and a conventional engine, increasing fuel economy and lowering pollutants. HEVs are characterized by their integration of diverse energy sources and power converters, typically the combination of an internal combustion engine (ICE) and electric motor. HEVs are currently regarded as a cost-effectiveness and may provide a potential solution for the foreseeable future [3]. The primary objective in developing HEVs is to minimize fuel consumption and emissions while simultaneously addressing the power requirements of drivers. This is achieved by exploring suitable energy management strategies that can effectively allocate and utilize energy sources in HEVs. Energ

FAQs

Why are energy management strategies critical for hybrid electric vehicles?

EMSs determine power distribution between the engine and motor, which directly affects fuel consumption and emissions while meeting driver power requirements.

What main difficulty appears when designing EMS for HEVs?

Designers must maximize fuel economy while optimizing vehicle performance under complex, nonlinear behaviors and varying driving situations.

How has machine learning changed the development of HEV EMS?

Recent work increasingly integrates machine learning and AI to improve EMS performance, enabling better identification of complex system patterns and adaptation to dynamic operating environments.

Aug 2, 2026

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