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Energies
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New Energy Vehicles: Battery Management and System Control
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New Energy Vehicles: Battery Management and System Control

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

Deadline for manuscript submissions: closed (25 April 2025) | Viewed by 7112

Special Issue Editor

Dr. Chunwei Zhang

E-Mail Website
Guest Editor
1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
2. College of Automotive Engineering, Jilin University, Changchun 130025, China
Interests: heat and mass transfer; computational fluid dynamics; new energy vehicles; vehicle batteries; transport in porous media

Special Issue Information

Dear Colleagues,

The advancement in battery technologies have propelled the evolution of new energy vehicles, e.g., Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs), and Fuel Cell Electric Vehicles (FCEVs) for the automotive industry. Due to the intricate nature of different power sources, it is imperative to develop effective battery thermal management systems (BTMSs) and design effective control systems to shift between potential operating modes. Thus, this Special Issue encourages researchers working in this field to share their latest developments in emerging battery management technologies and control systems to improve the EVs’ efficiency and safety.

Dr. Chunwei Zhang
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

  • battery electric vehicle
  • battery thermal management system
  • flow channels
  • artificial intelligence

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Published Papers (2 papers)

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Review

41 pages, 10379 KiB  
Review
Next Generation of Electric Vehicles: AI-Driven Approaches for Predictive Maintenance and Battery Management
by Muhammed Cavus, Dilum Dissanayake and Margaret Bell
Energies 2025, 18(5), 1041; https://doi.org/10.3390/en18051041 - 21 Feb 2025
Cited by 6 | Viewed by 3413
Abstract
This review explores recent advancements in electric vehicles (EVs), focusing on the transformative role of artificial intelligence (AI) in battery management systems (BMSs) and system control technologies. While EVs are integral to sustainable transportation, challenges remain in optimising battery longevity, energy efficiency, and [...] Read more.
This review explores recent advancements in electric vehicles (EVs), focusing on the transformative role of artificial intelligence (AI) in battery management systems (BMSs) and system control technologies. While EVs are integral to sustainable transportation, challenges remain in optimising battery longevity, energy efficiency, and safety. AI-driven techniques—such as machine learning (ML), neural networks (NNs), and reinforcement learning (RL)—enhance battery state of health (SOH) and state of charge (SOC) predictions, as well as temperature regulation, offering superior accuracy over traditional methods. Additionally, AI-powered control frameworks optimise energy distribution, regenerative braking, and power allocation under varying driving conditions. Deep RL enables adaptive, self-learning capabilities that improve energy efficiency and extend battery life, even in dynamic environments. This review also examines the integration of the Internet of Things (IoT) and big data analytics in EV systems, enabling predictive maintenance and fleet-level optimisation. By analysing these advancements, this paper highlights AI’s pivotal role in shaping next-generation, energy-efficient EVs. Full article
(This article belongs to the Special Issue New Energy Vehicles: Battery Management and System Control)
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38 pages, 10126 KiB  
Review
Advanced Deep Learning Techniques for Battery Thermal Management in New Energy Vehicles
by Shaotong Qi, Yubo Cheng, Zhiyuan Li, Jiaxin Wang, Huaiyi Li and Chunwei Zhang
Energies 2024, 17(16), 4132; https://doi.org/10.3390/en17164132 - 19 Aug 2024
Cited by 5 | Viewed by 3245
Abstract
In the current era of energy conservation and emission reduction, the development of electric and other new energy vehicles is booming. With their various attributes, lithium batteries have become the ideal power source for new energy vehicles. However, lithium-ion batteries are highly sensitive [...] Read more.
In the current era of energy conservation and emission reduction, the development of electric and other new energy vehicles is booming. With their various attributes, lithium batteries have become the ideal power source for new energy vehicles. However, lithium-ion batteries are highly sensitive to temperature changes. Excessive temperatures, either high or low, can lead to abnormal operation of the batteries, posing a threat to the safety of the entire vehicle. Therefore, developing a reliable and efficient Battery Thermal Management System (BTMS) that can monitor battery status and prevent thermal runaway is becoming increasingly important. In recent years, deep learning has gradually become widely applied in various fields as an efficient method, and it has also been applied to some extent in the development of BTMS. In this work, we discuss the basic principles of deep learning and related optimization principles and elaborate on the algorithmic principles, frameworks, and applications of various advanced deep learning methods in BTMS. We also discuss several emerging deep learning algorithms proposed in recent years, their principles, and their feasibility in BTMS applications. Finally, we discuss the obstacles faced by various deep learning algorithms in the development of BTMS and potential directions for development, proposing some ideas for progress. This paper aims to analyze the advanced deep learning technologies commonly used in BTMS and some emerging deep learning technologies and provide new insights into the current combination of deep learning technology in new energy trams to assist the development of BTMS. Full article
(This article belongs to the Special Issue New Energy Vehicles: Battery Management and System Control)
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