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Machines
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Dynamics and Control of Electric Vehicles
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Dynamics and Control of Electric Vehicles

A Special Issue of Machines (ISSN 2075-1702) belonging to the section "Vehicle Engineering".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2532

Editors

Dr. Ahmed A. Zaki Diab

E-Mail Website
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 (5 papers)

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Research

20 pages, 3927 KB  
Article
Real-Time Thermal Comfort-Oriented NMPC for Electric Vehicle Heat Pump Systems Using a Control-Oriented PMV Model
by Tai-Gon Kim, Hyunsang Wang, Wansik Choi and Changsun Ahn
Machines 2026, 14(9), 974; https://doi.org/10.3390/machines14090974 - 28 Aug 2026
Viewed by 214
Abstract
Cabin heating significantly affects the driving range of electric vehicles (EVs) under cold weather conditions, making energy-efficient thermal management essential. Conventional control strategies typically regulate a fixed cabin temperature setpoint, which may lead to suboptimal energy consumption and does not directly account for [...] Read more.
Cabin heating significantly affects the driving range of electric vehicles (EVs) under cold weather conditions, making energy-efficient thermal management essential. Conventional control strategies typically regulate a fixed cabin temperature setpoint, which may lead to suboptimal energy consumption and does not directly account for passenger thermal comfort. This study proposes a real-time thermal comfort-oriented nonlinear model predictive control (NMPC) framework for EV heat pump systems. To enable real-time implementation, a control-oriented model is developed by combining a data-driven model for heat pump performance with a linearized Predicted Mean Vote (PMV) model. The proposed NMPC optimizes the trade-off between passenger thermal comfort and compressor energy consumption while satisfying actuator constraints. A high-fidelity physics-based virtual plant is employed to evaluate the proposed strategy. Simulation results show that the proposed NMPC maintains thermal comfort within the recommended PMV range while reducing total energy consumption by 8.3% compared with a conventional rule-based controller. Furthermore, a parametric study involving 1210 gain-tuning cases of a rule-based controller and 100 NMPC weighting scenarios demonstrates a consistently superior comfort–energy trade-off. The average computation time remains below 0.15 s on the simulation platform, indicating that the proposed NMPC formulation can be solved within the selected sampling interval. These results highlight the potential of thermal comfort-oriented NMPC for improving both passenger comfort and energy efficiency in EV heat pump systems. Full article
(This article belongs to the Special Issue Dynamics and Control of Electric Vehicles)
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36 pages, 3973 KB  
Article
MPC-Informed Dynamic Screening for the Co-Design of Battery–Supercapacitor Hybrid Energy Storage Systems in Electric Vehicles
by Hanlin Lei, Benjamin Chong and Kang Li
Machines 2026, 14(8), 927; https://doi.org/10.3390/machines14080927 - 12 Aug 2026
Viewed by 265
Abstract
Hardware sizing and energy management for hybrid energy storage systems are usually designed sequentially, hiding the interactions between them. This paper proposes an MPC-informed dynamic screening framework in which every candidate configuration is simulated under one model predictive control law over a complete [...] Read more.
Hardware sizing and energy management for hybrid energy storage systems are usually designed sequentially, hiding the interactions between them. This paper proposes an MPC-informed dynamic screening framework in which every candidate configuration is simulated under one model predictive control law over a complete driving cycle, so that operational behaviour, not static metrics, determines selection. A fully documented post-evaluation criterion aggregates tracking, battery electrical stress, soft constraint violations and design overhead into one score normalised against an exact baseline anchor. Because one evaluation costs about 60 ms, the complete exact Pareto front of an electric transit bus case study is screened, not a sample. The static design cost proves almost uninformative regarding dynamic performance: the rank correlation between the two orderings is statistically indistinguishable from zero, the sets that they rank highest share no member, and the statically cheapest design falls far down the dynamic ranking, ending below the baseline. The cause is structural opposition on the pack voltage, which improves the dynamic performance but raises the static cost. The framework returns a leading design family that improves on the baseline overall, quantifies the battery stress that its leaner supercapacitor incurs, and shows the verdict to be robust to controller tuning but dependent on the duty and control strategy. Full article
(This article belongs to the Special Issue Dynamics and Control of Electric Vehicles)
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41 pages, 12151 KB  
Article
From Model to Embedded Implementation: Experimental Validation of PI and Takagi-Sugeno BLDC Speed Controllers for Electric Micromobility
by Mohamed Krichi, Mhamed Fannakh, Abdullah M. Noman, Tarik Raffak, Sulaiman Z. Almutairi and Abdullah M. Alharbi
Machines 2026, 14(8), 906; https://doi.org/10.3390/machines14080906 - 7 Aug 2026
Viewed by 327
Abstract
Speed controllers for electric micromobility (EMM) drives are increasingly developed with Model-Based Design and deployed as automatically generated code, yet the cost that a given control law actually imposes on the target, and the mechanism by which competing laws differ once deployed, are [...] Read more.
Speed controllers for electric micromobility (EMM) drives are increasingly developed with Model-Based Design and deployed as automatically generated code, yet the cost that a given control law actually imposes on the target, and the mechanism by which competing laws differ once deployed, are seldom reported. This paper addresses both questions on an EMM-class test bench built around a 36 V, 250 W in-wheel BLDC motor. A proportional-integral (PI) regulator and a first-order Takagi-Sugeno (TS) fuzzy regulator are specified in Simulink, auto-coded to ANSI-C by Embedded Coder, and deployed unchanged on an STM32F446RE target driving a custom three-phase inverter through six-step Hall commutation. Over a six-step, 180 s duty cycle reaching 21.1 km/h, the two regulators are shown to occupy opposite ends of the speed-versus-damping trade-off. On the 30 to 100 RPM ascending step under load, the PI reaches the set-point in 0.4±0.1 s with 21.6% overshoot and the TS in 2.7±0.1 s with 1.5% overshoot, both quoted at the resolution of the 10 Hz acquisition, and over the complete duty cycle, a window that also contains segments on which neither regulator has control authority, the TS lowers the tracking RMSE by 9.4%. A structural analysis of the deployed firmware excludes the realisation form as the cause. The positional and incremental forms are algebraically equivalent while the command is unsaturated, which is the regime of the step above. Under saturation, the incremental accumulator of the TS is not clamped and winds up exactly as the positional PI integrator does. The two loops are also shown to share the same unfiltered speed feedback and the same command saturation limits. The difference is traced instead to the effective gains realised by the seven consequents. Far from the set-point, the TS applies an integral gain three to twelve times weaker than the PI for a comparable proportional gain. A fixed-gain PI in that range is predicted to reproduce the response for one eighth of the Flash. The embedded cost of both regulators is then quantified on the target from the linker map, the fuzzy controller occupying 2325 Bytes of Flash against 266 Bytes for the PI, a factor of 8.7, and 200 Bytes of stack against 32 Bytes, a factor of 6.3, rising to 248 Bytes against 32 Bytes when the complete call tree is counted, for 0.45% of the available Flash. The complete platform, comprising the inverter, the Hall front end, the auto-generated firmware, and a Python supervisory interface, is described together with its deployed timing, PWM, and saturation parameters. Full article
(This article belongs to the Special Issue Dynamics and Control of Electric Vehicles)
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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 284
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 938
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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