Advances in Electrical and Autonomous Vehicles: Trends, Challenges and Prospects

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

Deadline for manuscript submissions: 15 October 2026 | Viewed by 858

Special Issue Editors


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Guest Editor
Instituto Universitario de Investigación del Automóvil (INSIA), Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: autonomous vehicles; cooperative services; assistance systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Instituto Universitario de Investigación del Automóvil (INSIA), Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: alternative propulsion systems; exhaust emissions

Special Issue Information

Dear Colleagues,

Among the pillars on which the vehicles of the future will be based are electrification and automation. These two aspects of vehicle development have significant technological implications and still present significant challenges that require research and development.

In the case of electrification, different forms of energy storage can be used depending on the type of vehicle to achieve greater efficiency, storage density, safety, etc. The physical configuration of the powertrain and its control strategies can also be optimized to achieve greater autonomy and operating ranges that allow for extended component lifespans.

Automated driving presents clear challenges related to environmental perception, with the need to achieve precise situational awareness and robust and explainable decision-making. New sensors, algorithms for processing information obtained from them, and sensor fusion are some areas of development related to information capture. Furthermore, artificial intelligence has demonstrated an ability to make decisions within complex driving scenarios.

For this reason, this Special Issue will consider all kinds of cutting-edge technological developments related to the electrification and automation of road vehicles. We welcome the submission of original research articles and reviews on research areas including (but not limited to) the following:

  • Electric powertrains
  • Batteries
  • Control strategies
  • Electric motors
  • Fuel cells
  • Sensor fusion
  • Perception algorithms
  • Decision algorithms
  • Automation architectures

We look forward to receiving your contributions.

Dr. Felipe Jiménez
Dr. José María López
Guest Editors

Manuscript Submission Information

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Keywords

  • electric vehicle
  • batteries
  • powertrain
  • automated vehicle
  • perception
  • decision-making

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Published Papers (1 paper)

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Research

21 pages, 1398 KB  
Article
Co-Design Method for Energy Management Systems in Vehicle–Grid-Integrated Microgrids From HIL Simulation to Embedded Deployment
by Yan Chen, Takahiro Kawaguchi and Seiji Hashimoto
Electronics 2026, 15(9), 1786; https://doi.org/10.3390/electronics15091786 - 22 Apr 2026
Viewed by 214
Abstract
With the widespread adoption of electric vehicles (EVs), the deep integration of transportation and power grids has emerged as a significant trend. EV charging stations, acting as dynamic loads, present challenges to real-time power balance and economic dispatch in microgrids, while EVs serving [...] Read more.
With the widespread adoption of electric vehicles (EVs), the deep integration of transportation and power grids has emerged as a significant trend. EV charging stations, acting as dynamic loads, present challenges to real-time power balance and economic dispatch in microgrids, while EVs serving as mobile energy storage units offer new opportunities for system flexibility. To address these issues, this paper proposes a hardware-in-the-loop (HIL) co-design method for vehicle–grid-integrated microgrid energy management systems, covering the entire workflow from simulation to embedded deployment. This method resolves the core challenges of multi-objective optimization algorithm deployment on embedded platforms (i.e., high computational complexity, strict real-time constraints, and heterogeneous communication protocol integration) via deployability analysis, hybrid code generation, real-time task restructuring, and consistency validation. A prototype microgrid system integrating photovoltaic panels, wind turbines, diesel generators, an energy storage system, and EV charging loads was built on the RK3588 embedded platform. An improved multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize operational costs. Experimental results verify the effectiveness of the proposed co-design method. Compared with traditional rule-based control strategies, the MOPSO algorithm reduces the total daily operating cost of the VGIM system by approximately 50%. After integrating vehicle-to-grid (V2G) scheduling, the operating cost is further reduced. In addition, this method ensures the consistency of algorithm functionality and performance during the migration from HIL simulation to embedded deployment, and the RK3588-based embedded system can complete a single optimization iteration within 60 s, which fully satisfies the real-time requirements of industrial applications. This work provides a feasible technical pathway for the reliable deployment of vehicle–grid-integrated microgrids in practical industrial scenarios. Full article
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