Dynamics, Control and Simulation of Electric Vehicles

Topic Information

Dear Colleagues,

Vehicle engineering has become an intersectional discipline with the development of electrification, intelligence, networking, and sharing technologies. Suitable vehicle system dynamics and advanced control methods are playing an increasingly crucial role in vehicle design. These technologies address mechanical engineering, electronic, and electrical engineering, control engineering, signal processing, and artificial intelligence, among others.

This Topic focuses on advanced vehicle system design, modelling, dynamic analysis, and control methods. Its topics of interest include, but are not limited to, the following:

• The advanced modelling and dynamic analysis of vehicle systems and their components, including steering, braking, suspension, chassis systems, and power train;
• The application of advanced observation methods for vehicle key dynamic parameters and verification;
• The application of intelligent vehicle fusion perception methods and advanced trajectory planning and control technology;
• Human–machine co-driving technology, driver modelling, and analysis of human factor engineering characteristics;
• Intelligent connected vehicles and road vehicle collaborative control technology.

Dr. Xiang Chen
Dr. Xiangyu Wang
Dr. Congzhi Liu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Energies energies	3.2	7.3	2008	16.2 Days	CHF 2600	Submit
Machines machines	2.5	4.7	2013	16.9 Days	CHF 2400	Submit
Modelling modelling	1.5	2.2	2020	19.5 Days	CHF 1200	Submit
Vehicles vehicles	2.2	5.3	2019	22.1 Days	CHF 1600	Submit
Applied Mechanics applmech	1.5	3.5	2020	20.4 Days	CHF 1400	Submit
Future Transportation futuretransp	1.7	3.8	2021	33.4 Days	CHF 1200	Submit
Technologies technologies	3.6	8.5	2013	21.8 Days	CHF 1600	Submit

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Published Papers (3 papers)

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All Energies Machines Modelling Vehicles Applied Mechanics Future Transportation Technologies

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20 pages, 2758 KB  

Open AccessArticle

Prediction of Battery Electric Vehicle Energy Consumption via Pre-Trained Model Under Inconsistent Feature Spaces

by Yizhou Wang, Haichao Huang, Ruimin Hao, Liangying Luo and Hong-Di He

Technologies 2025, 13(11), 493; https://doi.org/10.3390/technologies13110493 - 29 Oct 2025

Abstract

Accurately predicting the trip-level energy consumption of battery electric vehicles (BEVs) can alleviate range anxiety of drivers and improve intelligent route planning. However, although data-driven methods excel in predicting with multi-feature inputs, each vehicle often requires a dedicated model due to potential inconsistencies [...] Read more.

Accurately predicting the trip-level energy consumption of battery electric vehicles (BEVs) can alleviate range anxiety of drivers and improve intelligent route planning. However, although data-driven methods excel in predicting with multi-feature inputs, each vehicle often requires a dedicated model due to potential inconsistencies in feature spaces of collected data. Consequently, the necessity of sufficient trip data challenges newly registered vehicles. To address the challenges, this study proposed a transformer-based pre-trained model for BEV energy consumption prediction adapting to inconsistent feature spaces, referred to as IFS-Former. By innovatively introducing trainable missing-feature embeddings and placeholder masks, the IFS-Former can tolerate new or missing features of downstream tasks after pre-training. The IFS-Former was pre-trained on a dataset comprising 837 vehicles from 8 different cities, containing 492 thousand trips, and validated on 13 vehicles with inconsistent feature spaces. After applying transfer learning to the 13 vehicles, the pre-trained IFS-Former attains high prediction accuracy (R² = 0.97, mean absolute error (MAE) = 1.19). Even under extremely inconsistent feature spaces, the IFS-Former maintains robust performance (R² = 0.96, MAE = 1.31) leveraging its pre-trained knowledge. Furthermore, the IFS-Former is well-suited for on-board deployment with a size of only 32 MB. This study facilitates on-board artificial intelligence for accurate and practical energy consumption prediction. Full article

(This article belongs to the Topic Dynamics, Control and Simulation of Electric Vehicles)

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22 pages, 1778 KB  

Open AccessArticle

Event-Triggered and Adaptive ADMM-Based Distributed Model Predictive Control for Vehicle Platoon

by Hanzhe Zou, Hongtao Ye, Wenguang Luo, Xiaohua Zhou and Jiayan Wen

Vehicles 2025, 7(4), 115; https://doi.org/10.3390/vehicles7040115 - 3 Oct 2025

Viewed by 427

Abstract

This paper proposes a distributed model predictive control (DMPC) framework integrating an event-triggered mechanism and an adaptive alternating direction method of multipliers (ADMM) to address the challenges of constrained computational resources and stringent real-time requirements in distributed vehicle platoon control systems. Firstly, the [...] Read more.

This paper proposes a distributed model predictive control (DMPC) framework integrating an event-triggered mechanism and an adaptive alternating direction method of multipliers (ADMM) to address the challenges of constrained computational resources and stringent real-time requirements in distributed vehicle platoon control systems. Firstly, the longitudinal dynamic model and communication topology of the vehicle platoon are established. Secondly, under the DMPC framework, a controller integrating residual-based adaptive ADMM and an event-triggered mechanism is designed. The adaptive ADMM dynamically adjusts the penalty parameter by leveraging residual information, which significantly accelerates the solving of the quadratic programming (QP) subproblems of DMPC and ensures the real-time performance of the control system. In order to reduce unnecessary solver invocations, the event-triggered mechanism is employed. Finally, numerical simulations verify that the proposed control strategy significantly reduces both the computation time per optimization and the cumulative optimization instances throughout the process. The proposed approach effectively alleviates the computational burden on onboard resources and enhances the real-time performance of vehicle platoon control. Full article

(This article belongs to the Topic Dynamics, Control and Simulation of Electric Vehicles)

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24 pages, 1319 KB  

Open AccessArticle

Adaptive High-Order Sliding Mode Control for By-Wire Ground Vehicle Systems

by Ariadna Berenice Flores Jiménez, Stefano Di Gennaro, Maricela Jiménez Rodríguez and Cuauhtémoc Acosta Lúa

Technologies 2025, 13(10), 443; https://doi.org/10.3390/technologies13100443 - 1 Oct 2025

Viewed by 285

Abstract

This study focuses on the design and implementation of an Adaptive High-Order sliding mode control for by-wire ground vehicle systems. The controller integrates advanced technologies such as Active Front Steering (AFS) and Rear Torque Vectoring (RTV), aimed at enhancing vehicle dynamics. However, lateral [...] Read more.

This study focuses on the design and implementation of an Adaptive High-Order sliding mode control for by-wire ground vehicle systems. The controller integrates advanced technologies such as Active Front Steering (AFS) and Rear Torque Vectoring (RTV), aimed at enhancing vehicle dynamics. However, lateral velocity remains one of the most challenging variables to measure, even in modern vehicles. To address this limitation, a High-Order Sliding Mode (HOSM)-based observer with adaptive gains is proposed. The HOSM observer provides critical information for the operation of the dynamic controller, ensuring the tracking of desired references. Compared with traditional observers, the proposed adaptive HOSM observer achieves finite-time convergence of state estimation errors and exhibits enhanced robustness against external disturbances, as confirmed through simulation results. The adaptive gains dynamically adjust the system parameters, enhancing its precision and flexibility under changing environmental conditions. This dynamic approach ensures efficient and reliable performance, enabling the system to respond effectively to complex scenarios. The stability of the dynamic HOSM controller with adaptive gain is analyzed through a Lyapunov-based approach, providing solid theoretical guarantees. Its performance is evaluated using detailed simulations conducted in CarSim 2017 software. The simulation results demonstrate that the proposed controller is highly effective in ensuring accurate trajectory tracking. Full article

(This article belongs to the Topic Dynamics, Control and Simulation of Electric Vehicles)

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