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Electronics
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Advances in Electric Vehicles and Energy Storage Systems
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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 2026 | Viewed by 2637

Special Issue Editors

Dr. Chunchun Jia

E-Mail Website
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
Prof. Dr. Fengyan Yi

E-Mail Website
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
Prof. Dr. Chaofeng Pan

E-Mail Website
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

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 250 words) can be sent to the Editorial Office for assessment.

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. Electronics 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 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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

20 pages, 1995 KB  
Article
Optimized PAB-RRT Algorithm for Autonomous Vehicle Path Planning in Complex Scenarios
by Jinbo Wang, Weihai Zhang, Jinming Zhang, Wei Liao and Tingwei Du
Electronics 2026, 15(3), 651; https://doi.org/10.3390/electronics15030651 - 2 Feb 2026
Abstract
Path planning is a pivotal technology for autonomous vehicles, directly influencing driving safety and comfort. Developing algorithms adaptable to diverse scenarios is critical for ensuring the safe operation of autonomous driving systems and advancing their engineering applications. The existing Rapidly exploring Random Tree [...] Read more.
Path planning is a pivotal technology for autonomous vehicles, directly influencing driving safety and comfort. Developing algorithms adaptable to diverse scenarios is critical for ensuring the safe operation of autonomous driving systems and advancing their engineering applications. The existing Rapidly exploring Random Tree (RRT) algorithm has limitations such as low efficiency and tortuous, lengthy paths. To address these issues, this study proposes the PAB-RRT algorithm, which integrates probabilistic goal bias, adaptive step size, and bidirectional exploration into RRT. Comparative simulations were conducted to evaluate PAB-RRT against traditional RRT, RRT*, and single-strategy improved variants (A-RRT, P-RRT, B-RRT). Results show that in static multi-obstacle scenarios, PAB-RRT completes planning with 30 iterations (6.99% of traditional RRT), 0.1255 s computation time (21.9% of traditional RRT), and a 130.83 m path length (7.2% shorter than traditional RRT). In dynamic obstacle scenarios, it requires 19 iterations (0.0434 s) at the initial stage and 37 iterations (0.0861 s) after obstacle movement, with path length stably around 130 m. Overall, PAB-RRT outperforms traditional algorithms in exploration efficiency, path performance, and robustness in complex settings, better meeting the efficiency and reliability requirements of autonomous vehicle path planning under complex scenarios and providing a feasible reference for related technology. Full article
(This article belongs to the Special Issue Advances in Electric Vehicles and Energy Storage Systems)
17 pages, 5741 KB  
Article
An Explainable Fault Diagnosis Algorithm for Proton Exchange Membrane Fuel Cells Integrating Gramian Angular Fields and Gradient-Weighted Class Activation Mapping
by Xing Shu, Fengyan Yi, Jinming Zhang, Jiaming Zhou, Shuo Wang, Hongtao Gong and Shuaihua Wang
Electronics 2025, 14(22), 4401; https://doi.org/10.3390/electronics14224401 - 12 Nov 2025
Cited by 1 | Viewed by 547
Abstract
Reliable operation of proton exchange membrane fuel cells (PEMFCs) is crucial for their widespread commercialization, and accurate fault diagnosis is the key to ensuring their long-term stable operation. However, traditional fault diagnosis methods not only lack sufficient interpretability, making it difficult for users [...] Read more.
Reliable operation of proton exchange membrane fuel cells (PEMFCs) is crucial for their widespread commercialization, and accurate fault diagnosis is the key to ensuring their long-term stable operation. However, traditional fault diagnosis methods not only lack sufficient interpretability, making it difficult for users to trust their diagnostic decisions, but also one-dimensional (1D) feature extraction methods highly rely on manual experience to design and extract features, which are easily affected by noise. This paper proposes a new interpretable fault diagnosis algorithm that integrates Gramian angular field (GAF) transform, convolutional neural network (CNN), and gradient-weighted class activation mapping (Grad-CAM) for enhanced fault diagnosis and analysis of proton exchange membrane fuel cells. The algorithm is systematically validated using experimental data to classify three critical health states: normal operation, membrane drying, and hydrogen leakage. The method first converts the 1D sensor signal into a two-dimensional GAF image to capture the temporal dependency and converts the diagnostic problem into an image recognition task. Then, the customized CNN architecture extracts hierarchical spatiotemporal features for fault classification, while Grad-CAM provides visual explanations by highlighting the most influential regions in the input signal. The results show that the diagnostic accuracy of the proposed model reaches 99.8%, which is 4.18%, 9.43% and 2.46% higher than other baseline models (SVM, LSTM, and CNN), respectively. Furthermore, the explainability analysis using Grad-CAM effectively mitigates the “black box” problem by generating visual heatmaps that pinpoint the key feature regions the model relies on to distinguish different health states. This validates the model’s decision-making rationality and significantly enhances the transparency and trustworthiness of the diagnostic process. Full article
(This article belongs to the Special Issue Advances in Electric Vehicles and Energy Storage Systems)
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25 pages, 10814 KB  
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
Cited by 1 | Viewed by 1202
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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