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Machines
Special Issues
Dynamics and Control of Electric Vehicles
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Dynamics and Control of Electric Vehicles

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1281

Editors

Dr. Ahmed A. Zaki Diab

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Guest Editor
Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia 61517, Egypt
Interests: renewable energy systems; power electronics; machines drives; smart grids; evolutionary; heuristic optimization techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Hamdy M. Sultan

E-Mail
Guest Editor
Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia 61517, Egypt
Interests: power system operation; renewable energy; optimization; power system stability; energy management; smart grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid electrification of transportation represents not merely an evolution in vehicular technology but a profound transformation of the broader energy ecosystem. Electric vehicles (EVs) are complex cyber-physical systems where high-fidelity modeling, sophisticated dynamic analysis, and robust hierarchical control strategies are paramount. These imperatives extend beyond the vehicle itself to encompass charging infrastructure, bidirectional energy exchange (V2G/G2V), and the large-scale, systemic impact of EV fleets on power grid stability, planning, and economics. Advances in these interconnected domains—spanning battery electrochemistry, traction drive performance, power electronics reliability, and intelligent grid interaction—are critical to achieving superior system efficiency, safety, reliability, and sustainability. This Special Issue aims to consolidate cutting-edge research that bridges theoretical innovation with practical implementation, providing a comprehensive resource for engineers and researchers shaping the future of transportation and energy.

Dr. Ahmed A. Zaki Diab
Dr. Hamdy M. Sultan
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. Machines is an international peer-reviewed open access monthly 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
  • dynamic modeling
  • control systems
  • energy management
  • battery systems
  • power electronics
  • EV charging infrastructure
  • vehicle-to-grid (V2G)
  • grid integration
  • power system stability
  • optimization
  • mechatronic systems
  • digital twin
  • machine learning

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Published Papers (2 papers)

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Research

22 pages, 781 KB  
Article
A Fault-Tolerant Finite-Control-Set MPC Architecture with Asymmetry-Aware Thermal Balancing for Switched Reluctance Motor Drives
by Franklin Sánchez, María Isabel Milanés-Montero and Enrique Romero-Cadaval
Machines 2026, 14(7), 817; https://doi.org/10.3390/machines14070817 - 18 Jul 2026
Viewed by 99
Abstract
Switched reluctance motors (SRMs) are attractive for fault-tolerant drives because their rare-earth-free rotor and intrinsic phase isolation support continued operation after a converter fault. Realising this requires a post-fault control policy that preserves both torque tracking and per-phase thermal balance, with the latter [...] Read more.
Switched reluctance motors (SRMs) are attractive for fault-tolerant drives because their rare-earth-free rotor and intrinsic phase isolation support continued operation after a converter fault. Realising this requires a post-fault control policy that preserves both torque tracking and per-phase thermal balance, with the latter being a safety-relevant design consideration motivated by—though not herein verified against—ISO 26262. This paper proposes and evaluates, by simulation, a three-layer fault-tolerant finite-control-set model predictive control (FCS-MPC) architecture for a four-phase 8/6 SRM under a single open-phase converter fault. The layers are (i) a vector-set reconfiguration from the eight healthy, active vectors to the twenty-six admissible post-fault vectors, which restores controllability of the reduced converter; (ii) soft commutation expressed as a position-dependent penalty inside the MPC cost; and (iii) asymmetry-aware balancing that evens out the accumulated thermal load across the three healthy phases. We additionally analyse an activated-on-demand max-penalty thermal limiter and show, both analytically and in simulation, that it shares its optimiser with the variance-based balancing term and therefore confers no measurable benefit over it; it is consequently retained only as an optional on-demand limiter rather than a separate layer. The architecture is benchmarked against a fault-blind baseline, a rule-based hard fault-tolerant reference (Hard-FT), and intermediate configurations through a deterministic ablation across three critical operating points, complemented by a robustness assessment under measurement noise and parameter mismatch. A six-criteria fault-tolerance scorecard is reported as a methodological observation on the transferability of healthy-mode SRM specifications to post-fault operation. Full article
(This article belongs to the Special Issue Dynamics and Control of Electric Vehicles)
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27 pages, 2382 KB  
Article
EST-GNN: An Explainable Spatio-Temporal Graph Framework with Lévy-Optuna Optimization for CO2 Emission Forecasting in Electrified Transportation
by Rabab Hamed M. Aly, Shimaa A. Hussien, Marwa M. Ahmed and Aziza I. Hussein
Machines 2026, 14(5), 463; https://doi.org/10.3390/machines14050463 - 22 Apr 2026
Viewed by 776
Abstract
The accurate and explainable prediction of carbon emissions is crucial for the efficient operation of hybrid and electrified transportation systems and their integration with energy grids. An Explainable Spatio-Temporal Graph Neural Network (EST-GNN) is proposed for highly precise CO2 emission forecasting using [...] Read more.
The accurate and explainable prediction of carbon emissions is crucial for the efficient operation of hybrid and electrified transportation systems and their integration with energy grids. An Explainable Spatio-Temporal Graph Neural Network (EST-GNN) is proposed for highly precise CO2 emission forecasting using Lévy Flight-guided Optuna optimization. By modelling vehicles and their operational characteristics as nodes in a dynamic graph, the proposed framework can jointly learn timing and spatial correlations while sustaining interpretability. The accuracy of the EST-GNN model is compared with models based on one-hot encoded features, SMOTE-enhanced datasets, and ensemble regressors. Using a real-world dataset of 7385 vehicle registrations with 12 predictive features experiments are conducted. When applied the EST-GNN model outperformed all baseline and traditional models achieving the highest reliability (R2 = 0.98754) while solving competitive error metrics (RMSE = 6.55, MAE = 2.556). There is strong indication that reasonable machine learning (ML) models can be used accurately to confirm their suitability for resource-prevented and real-time applications, while predictable ML techniques have relatively low reliability. The optimal solution ensures scalability, robustness, and independence of the deployment environment. The distribution analysis of best performing models develops the ability of EST-GNN, which accounts for the largest proportion of best results across evaluation metrics. To achieve superior predictive accuracy, graph-based learning, explainability, and advanced hyperparameter optimization are combined. EST-GNN provides a powerful tool for analyzing fleet emission levels, making energy-aware decisions, and planning sustainable transportation, while ML models continue to be a useful complement for deployment states with high computation costs and quick responses. Full article
(This article belongs to the Special Issue Dynamics and Control of Electric Vehicles)
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