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Optimized Energy Management Technology for Electric Vehicle

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: 29 September 2025 | Viewed by 536

Special Issue Editor

Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
Interests: dynamics and energy management; control of vehicle systems

Special Issue Information

Dear Colleagues,

The rapid adoption of electric vehicles (EVs) has become a cornerstone of the global transition toward sustainable transportation. However, the efficient management of energy in EVs remains a critical challenge, directly impacting their performance, range, and overall sustainability. Optimized energy management technologies are essential to maximize the utilization of energy resources, enhance battery life, and improve the overall efficiency of EV systems.

This Special Issue, titled "Optimized Energy Management Technology for Electric Vehicle", aims to showcase the latest advancements in energy management strategies, technologies, and methodologies tailored for electric vehicles. We invite contributions that address the design, modeling, optimization, and implementation of energy management systems (EMSs) for EVs, as well as their integration with renewable energy sources, smart grids, and other emerging technologies.

Topics of interest for publication include, but are not limited to, the following:

  • Advanced energy management strategies for EVs;
  • Optimization of battery performance and thermal management;
  • Integration of EVs with renewable energy systems and smart grids;
  • Machine learning and AI-based approaches for energy management;
  • Power electronics and control techniques for EV energy systems;
  • Vehicle dynamics;
  • Intelligent energy management for fuel cell vehicles;
  • Novel energy storage technologies for EVs;
  • Real-time monitoring and fault diagnosis in EV energy systems;
  • Intelligent energy-saving control for electric vehicles.

We encourage researchers and practitioners to submit their original research, reviews, and case studies that contribute to the advancement of optimized energy management technologies for electric vehicles.

Dr. Bin Huang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electric vehicles (EVs)
  • energy management
  • optimization techniques
  • battery optimization
  • vehicle dynamics

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Published Papers (1 paper)

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Review

42 pages, 8877 KiB  
Review
Artificial-Intelligence-Based Energy Management Strategies for Hybrid Electric Vehicles: A Comprehensive Review
by Bin Huang, Wenbin Yu, Minrui Ma, Xiaoxu Wei and Guangya Wang
Energies 2025, 18(14), 3600; https://doi.org/10.3390/en18143600 - 8 Jul 2025
Viewed by 298
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Optimized Energy Management Technology for Electric Vehicle)
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