Advances in Electric Vehicles and Energy Storage Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 665

Special Issue Editors


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Guest Editor
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: electric vehicles; battery systems; fuel cell systems; energy management strategy

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Guest Editor
School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
Interests: key technologies of new energy vehicles; energy management and control
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute of Automotive Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: energy storage; electric vehicles; regenerative braking; battery thermal management; energy management; automotive engineering; battery management system

Special Issue Information

Dear Colleagues,

With the continuous advancement of artificial intelligence technology, future electric vehicles and energy storage systems will evolve towards being more intelligent, safe, and environmentally friendly. This Special Issue focuses on cutting-edge research in electric vehicles and energy storage systems, aiming to explore key technologies related to these fields in depth, including design, optimization, and intelligent control. By integrating artificial intelligence with advanced control technologies, the development of electric vehicles and energy storage systems will be further advanced in terms of intelligence, efficiency enhancement, and reliability. As a platform for disseminating pioneering research findings, this Special Issue will showcase results that are expected to have a significant impact on future solutions in the fields related to electric vehicles and energy storage systems.

This Special Issue welcomes original research papers and reviews, focusing on, but not limited to, the following topics:

  1. Prognostics and health management of energy storage systems;
  2. Intelligent energy management for fuel cell vehicles;
  3. Optimization and intelligent control of fuel cell systems;
  4. Intelligent batteries;
  5. Lifetime prediction of fuel cell systems;
  6. Battery optimization and management;
  7. Machine learning-based technology for autonomous driving;
  8. Eco-driving control;
  9. Artificial intelligence applications in electric vehicles and energy storage systems;
  10. Vehicle dynamics;
  11. Intelligent control of linear chassis.

Dr. Chunchun Jia
Prof. Dr. Fengyan Yi
Prof. Dr. Chaofeng Pan
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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
  • fuel cells
  • intelligent battery
  • autonomous driving
  • eco-driving
  • machine learning

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Published Papers (1 paper)

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Research

25 pages, 10814 KiB  
Article
Eco-Cooperative Planning and Control of Connected Autonomous Vehicles Considering Energy Consumption Characteristics
by Chaofeng Pan, Jintao Pi and Jian Wang
Electronics 2025, 14(8), 1646; https://doi.org/10.3390/electronics14081646 - 18 Apr 2025
Viewed by 147
Abstract
Cooperative driving systems can coordinate individual vehicles on the road in a platoon, holding significant promise for enhancing traffic efficiency and lowering the energy consumption of vehicle movements. For an extended period, vehicles on the road will consist of a mix of traditional [...] Read more.
Cooperative driving systems can coordinate individual vehicles on the road in a platoon, holding significant promise for enhancing traffic efficiency and lowering the energy consumption of vehicle movements. For an extended period, vehicles on the road will consist of a mix of traditional gasoline and electric vehicles. To explore the economic driving strategies for diverse vehicles on the road, this paper introduces a collaborative eco-driving system that takes into account the energy consumption traits of vehicles. Unlike prior research, this paper puts forward a lane change decision-making approach that integrates energy modeling and speed prediction. This method can effectively capture the speed variations in the vehicle ahead and facilitate lane changes with energy efficiency in mind. The system encompasses three vital functions: vehicle cooperative architecture, ecological trajectory planning, and power system control. Specifically, eco-speed planning is carried out in two stages: the initial stage is executed globally, with cooperative speed optimization performed based on the energy consumption characteristics of different vehicles to determine the economical speed for vehicle platoon driving. The subsequent stage involves local speed adaptation, where the vehicle platoon dynamically adjusts its speed and makes lane change decisions according to local driving conditions. Ultimately, the generated control information is fed into the powertrain control system to regulate the vehicle. To assess the proposed collaborative eco-driving system, the algorithms were tested on highways, and the results substantiated the system’s efficacy in reducing the energy consumption of vehicle driving. Full article
(This article belongs to the Special Issue Advances in Electric Vehicles and Energy Storage Systems)
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