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Electronics
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Advances in Electrical and Autonomous Vehicles: Trends, Challenges and Prospects
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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 1836

Editors

Dr. Felipe Jiménez

grade E-Mail Website
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
Dr. José María López

E-Mail Website
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

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

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Published Papers (4 papers)

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Research

15 pages, 4598 KB  
Article
Successive Reference-Pose Tracking for Delay-Robust Vehicle Teleoperation: A Real-World Experimental Evaluation
by Jai Prakash, Mattia Belloni, Michele Vignati and Edoardo Sabbioni
Electronics 2026, 15(12), 2743; https://doi.org/10.3390/electronics15122743 (registering DOI) - 22 Jun 2026
Abstract
Network latency remains a fundamental bottleneck in vehicle teleoperation, inducing instability and performance degradation in conventional control methods, while predictive techniques like the Smith Predictor offer a theoretical solution, their efficacy is often compromised by unmodelled dynamics and real-world disturbances. This paper presents [...] Read more.
Network latency remains a fundamental bottleneck in vehicle teleoperation, inducing instability and performance degradation in conventional control methods, while predictive techniques like the Smith Predictor offer a theoretical solution, their efficacy is often compromised by unmodelled dynamics and real-world disturbances. This paper presents the first experimental validation of the Successive Reference-Pose Tracking (SRPT) architecture. By streaming future reference poses rather than direct steering commands, SRPT leverages an onboard Nonlinear Model Predictive Controller to compute optimal vehicle actions while inherently accounting for dynamic constraints and network delays. Real-world human-in-the-loop experiments were conducted with four drivers on a test track featuring cornering, double lane-change, and slalom manoeuvres. Quantitative comparisons at 10 km/h across four modes—manual driving, direct teleoperation, a Smith Predictor, and SRPT—demonstrate that SRPT significantly outperforms other teleoperation methods, reducing cross-track error by up to 66% and yielding smoother, more stable control inputs. Furthermore, SRPT uniquely maintained stability during a proof-of-concept trial at 13 km/h, where it proactively moderated vehicle speed to respect actuator limits—a critical safety behavior absent in other modes. This work provides the first tangible evidence that SRPT is a robust and superior framework for delay-resilient vehicle teleoperation in real-world conditions. Full article
(This article belongs to the Special Issue Advances in Electrical and Autonomous Vehicles: Trends, Challenges and Prospects)
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25 pages, 2302 KB  
Article
Path Tracking for Autonomous Vehicles Integrating HOCBF-Based Reinforcement Learning and Model Predictive Control
by Zhengyu Song, Wenxin Wen, Junze Li, Junjie Wang, Minghui Ye, Mengna Li, Bowen Li, Zhuo Wang, Changqun Sun, Aidong Luan, Meng Zhang, Changpeng Liu, Yantao Si and Bo Leng
Electronics 2026, 15(10), 2031; https://doi.org/10.3390/electronics15102031 - 10 May 2026
Viewed by 273
Abstract
High-precision path tracking is crucial for the safe operation of autonomous vehicles. However, the performance of Model Predictive Control (MPC) depends heavily on the accuracy of the vehicle dynamics model, whereas Deep Reinforcement Learning (DRL) generally lacks formal safety guarantees during training and [...] Read more.
High-precision path tracking is crucial for the safe operation of autonomous vehicles. However, the performance of Model Predictive Control (MPC) depends heavily on the accuracy of the vehicle dynamics model, whereas Deep Reinforcement Learning (DRL) generally lacks formal safety guarantees during training and exploration. To address this issue, this paper proposes a hybrid path-tracking framework, termed CRL-MPC, which integrates High-Order Control Barrier Function (HOCBF)-based reinforcement learning feedforward control with model predictive feedback control. Specifically, a Deep Deterministic Policy Gradient (DDPG) agent generates nominal feedforward steering commands, which are then corrected online by a High-Order Control Barrier Function (HOCBF)-based safety filter through a Quadratic Programming (QP) problem. During training on a high-fidelity CarSim–Simulink–Python co-simulation platform, the HOCBF-based safety filter constrains exploration within physically feasible regions, thereby preventing simulator failure caused by dynamically unsafe actions and improving training stability and sample efficiency. Meanwhile, the MPC controller provides feedback correction to compensate for residual errors. Comparative simulations were conducted against two baseline architectures: a standalone conventional MPC controller and a reinforcement-learning-based MPC (RL-MPC) hybrid architecture without the HOCBF-based safety filter. The results show that CRL-MPC achieves superior overall performance in path-tracking accuracy, control smoothness, and lateral dynamic stability. Compared with conventional MPC, CRL-MPC reduces the maximum lateral displacement error and its root mean square error (RMSE) by 54.1% and 62.7%, respectively, and reduces the maximum heading-angle error and its RMSE by 18.1% and 27.1%, respectively. Full article
(This article belongs to the Special Issue Advances in Electrical and Autonomous Vehicles: Trends, Challenges and Prospects)
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28 pages, 2633 KB  
Article
Data-Driven Analysis of Electric Powertrain Energy Flow and Traction Battery Behavior in a Modern Battery Electric Vehicle Using Real-World OBD Data
by Jacek Caban, Branislav Šarkan, Arkadiusz Małek, Szymon Dowkontt and Michal Loman
Electronics 2026, 15(10), 2018; https://doi.org/10.3390/electronics15102018 - 9 May 2026
Viewed by 439
Abstract
This study presents a data-driven analysis of electric powertrain energy flow and traction battery behavior in a modern battery electric vehicle based on real-world on-board diagnostic (OBD) measurements. Time-resolved signals acquired during an urban trip by a Renault 5 E-Tech Electric were processed [...] Read more.
This study presents a data-driven analysis of electric powertrain energy flow and traction battery behavior in a modern battery electric vehicle based on real-world on-board diagnostic (OBD) measurements. Time-resolved signals acquired during an urban trip by a Renault 5 E-Tech Electric were processed to reconstruct instantaneous energy exchange between the traction system and the battery, identify distinct operating regimes, and derive physically interpretable empirical models of selected drivetrain relationships. The analysis focused on the traction power, battery current, battery voltage, state of charge, accelerator pedal position, and cell voltage imbalance. The recorded data were decomposed into propulsion, regenerative, and auxiliary-load-dominated operating regimes, which improved the interpretability of the measured signals and the quality of the regression-based models. A second-order model was used to describe the relationship between traction power and accelerator pedal position, while a linear current-voltage model provided a locally accurate approximation of battery electrical behavior. In addition, the dependence of the cell voltage imbalance on the battery current was analyzed as a diagnostic indicator of load-dependent battery response. The results show that auxiliary loads, especially cabin and battery heating under winter conditions, introduce a significant baseline power demand that affects the apparent drivetrain response. Within the analyzed single-trip dataset, the recorded battery signals showed a low cell-voltage imbalance and a consistent local current–voltage trend over the observed operating range. These findings should be interpreted as preliminary and vehicle-specific, since they were obtained from one short winter urban trip and from a restricted set of OBD-accessible signals. Although the study is limited to a single vehicle and a single short trip, it demonstrates that accessible real-world OBD data can support physically interpretable, exploratory analysis of electric powertrain operation and battery response under practical measurement constraints. Full article
(This article belongs to the Special Issue Advances in Electrical and Autonomous Vehicles: Trends, Challenges and Prospects)
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20 pages, 4082 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 337
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
(This article belongs to the Special Issue Advances in Electrical and Autonomous Vehicles: Trends, Challenges and Prospects)
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