Feature Papers in Propulsion Systems and Components in Electric Vehicle
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Editor
Prof. Dr. Vladimir Katic
Prof. Dr. Vladimir Katic
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Collection Editor
Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia
Interests: power electronics converters; power quality; renewable energy sources; electric vehicles; charging infrastructure
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Topical Collection Information
Dear Colleagues,
This Topical Collection of the World Electric Vehicle Journal in the section Propulsion Systems and Components aims to collect research articles, reviews, and perspectives on the fast-evolving area of the electric train drives and their components in new energy vehicles. Efficiency and innovation in these systems are critical for the performance of electric and electrified vehicles. Submissions should include the latest research results or in-depth and essential insights in drive and propulsion systems, electric motor drives, and innovative designs of electric machines. Supporting systems, such as auxiliary components and sensors, vehicle motion and stability control, and chassis systems for EVs, are also within scope. Manuscripts should ideally fall into the following fields:
- Drive and propulsion systems;
- Electric motor drive;
- Electric machine;
- Auxiliary components and Sensors;
- Vehicle motion and stability control;
- Chassis systems for EVS.
Prof. Dr. Vladimir Katic
Collection Editor
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. 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 collection 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. World Electric Vehicle Journal 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 1400 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
- drive and propulsion systems
- electric motor drive
- electric machine
- auxiliary components and sensors
- vehicle motion and stability control
- chassis systems for EVS
Published Papers (3 papers)
2026
Open AccessReview
Modern Approaches to Assessing the Technical Condition of Traction Lithium-Ion Batteries: Review Article
by
Yuri Katsuba, Mikhail Kochegarov, Andrey Zalyubovsky, Alexander Sivov and Alexander Bazhenov
Viewed by 263
Abstract
In the context of the rapid growth of the electric and hybrid vehicle fleet, ensuring the reliability, safety, and durability of traction lithium-ion battery packs has become a key scientific and engineering challenge. The technical condition of battery packs, characterized by such parameters
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In the context of the rapid growth of the electric and hybrid vehicle fleet, ensuring the reliability, safety, and durability of traction lithium-ion battery packs has become a key scientific and engineering challenge. The technical condition of battery packs, characterized by such parameters as state of charge (SOC), state of health (SOH), and remaining useful life (RUL), directly affects vehicle performance and the total cost of ownership of electric vehicles. This review article systematizes and analyzes current approaches to assessing the technical condition of battery packs. Fundamental degradation mechanisms and factors are considered, including operational, thermal, and mechanical effects. A detailed analysis is presented for the three main classes of diagnostic methods: model-based approaches, data-driven approaches (machine learning and deep learning), and hybrid methods combining the advantages of the former two. Particular attention is paid to methods for early fault detection, thermal runaway prediction, and condition assessment based on real-world operational data. The article presents quantitative results demonstrating the accuracy and effectiveness of various algorithms and also discusses key challenges and promising research directions, such as the use of cloud platforms, digital twins, and explainable artificial intelligence methods.
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Open AccessArticle
A Physics-Informed Hybrid Ensemble for Robust and High-Fidelity Temperature Forecasting in PMSMs
by
Rifath Bin Hossain, Md Maruf Al Hasan, Md Imran Khan, Monzur Ahmed, Yuting Lin and Xuchao Pan
Cited by 1 | Viewed by 591
Abstract
The deployment of artificial intelligence in safety-critical industrial systems is hindered by a core trust deficit, as models trained via empirical risk minimization often fail catastrophically in out-of-distribution (OOD) scenarios. We address this challenge by developing a physics-informed hybrid ensemble that achieves state-of-the-art
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The deployment of artificial intelligence in safety-critical industrial systems is hindered by a core trust deficit, as models trained via empirical risk minimization often fail catastrophically in out-of-distribution (OOD) scenarios. We address this challenge by developing a physics-informed hybrid ensemble that achieves state-of-the-art accuracy and robustness for Permanent Magnet Synchronous Motor (PMSM) temperature forecasting. Our methodology first calibrates a Lumped-Parameter Thermal Network (LPTN) to serve as a physics engine for generating physically consistent data augmentations, which then pre-trains a Temporal Convolutional Network (TCN) encoder via self-supervision, with the final prediction assembled from the physics model’s baseline guess and a correction learned by an ensemble of gradient boosting models on a rich, multi-modal feature set. Evaluated against a suite of strong baselines, our hybrid ensemble achieves a state-of-the-art Root Mean Squared Error of 5.24 °C on a challenging OOD stress test composed of the most chaotic operational profiles. Most compellingly, our model’s performance improved by an unprecedented −10.68% under these extreme stress conditions where standard, purely data-driven models collapsed. This demonstrated robustness, combined with a statistically valid Coverage Under Shift (CUS) Gap of only 1.43%, provides a complete blueprint for building high-performance, trustworthy AI, enabling safer and more efficient control of critical cyber-physical systems and motivating future research into physics-guided pre-training for other industrial assets.
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Open AccessArticle
Research on the Dynamic Performance of a New Semi-Active Hydro-Pneumatic Suspension System Based on GA-MPC Strategy
by
Ruochen Wang, Xiangwen Zhao, Renkai Ding and Jie Chen
Cited by 1 | Viewed by 449
Abstract
To address the limited capability of conventional hydro-pneumatic suspensions in coordinated damping–stiffness regulation, this paper proposes a new semi-active hydro-pneumatic suspension (SAHPS) system based on a dual-valve shock absorber. A damping valve architecture composed of a spring check valve–solenoid proportional valve–spring check valve
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To address the limited capability of conventional hydro-pneumatic suspensions in coordinated damping–stiffness regulation, this paper proposes a new semi-active hydro-pneumatic suspension (SAHPS) system based on a dual-valve shock absorber. A damping valve architecture composed of a spring check valve–solenoid proportional valve–spring check valve is arranged between the rod and rodless chambers of the hydraulic cylinder, enabling coordinated adjustment of suspension damping and equivalent stiffness. Furthermore, a genetic algorithm optimization with model predictive control (GA-MPC) is designed to enhance the overall dynamic performance of the suspension while effectively reducing the operating frequency of the solenoid proportional valve. Finally, AMESim–Simulink co-simulations and hardware-in-the-loop (HIL) experiments are conducted under bumpy road excitation and Class C random road conditions. Under Class C random road conditions, compared with passive hydro-pneumatic suspension and semi-active suspension with conventional MPC, the proposed method achieves maximum reductions of 11%, 25%, and 12.9% in the root mean square values of body acceleration, suspension working space, and dynamic tire load, respectively. The discrepancies between experimental and simulation results remain below 7%, confirming the effectiveness of the proposed system and control strategy. This study provides a new technical guidance for low-frequency vibration suppression in vehicle suspension systems.
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