Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.6
Journals
WEVJ
Special Issues
Dynamic Modeling, Identification, and Advanced Control of Intelligent Electric Vehicles
share announcement

Dynamic Modeling, Identification, and Advanced Control of Intelligent Electric Vehicles

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 30 November 2025 | Viewed by 8249

Special Issue Editor

Dr. Te Chen

E-Mail Website
Guest Editor
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
Interests: vehicle dynamics and control; design and advanced control of electrified chassis; decision planning; control of intelligent vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electrification and intelligence are trends in the development of automotive technology. At the same time, the development of electrification and intelligent technology in automobiles has also put forward new demands for vehicle chassis control. The requirements for modeling accuracy and control system integration are becoming increasingly strict for intelligent chassis that integrates vehicle carrying capacity, handling, smoothness, and safety. Vehicle dynamics modeling technology is a key link in achieving efficient, stable, and safe operation of vehicles, which can provide an important basis for in-depth understanding of vehicle dynamics characteristics and formulation and optimization of control strategies.

With the development of smart cars, more precise sensors and measurement technologies have provided more possibilities for data collection, and the development of machine learning and artificial intelligence algorithms has also provided new means for data processing and model optimization. Vehicle control systems are gradually moving towards a highly integrated direction, and advanced control algorithms are the core of intelligent electric vehicle dynamics control technology. Relevant research can provide strong support for the optimization of vehicle control systems.

Dr. Te Chen
Guest 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. 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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • vehicle dynamics
  • smart cars
  • vehicle control systems
  • advanced control algorithms
  • intelligent electric vehicle

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

21 pages, 3549 KiB  
Article
Research on the Performance of Vehicle Lateral Control Algorithm Based on Vehicle Speed Variation
by Weihai Zhang, Jinbo Wang and Tongjia Pang
World Electr. Veh. J. 2025, 16(5), 259; https://doi.org/10.3390/wevj16050259 - 4 May 2025
Viewed by 140
Abstract
Analyzing the performance characteristics and applicable environments of intelligent vehicle lateral control algorithms, this paper establishes the Model Predictive Control (MPC), Pure Pursuit (PP), and Linear Quadratic Regulator (LQR) algorithms and uses 2022b MATLAB software to simulate the lateral error, heading error, and [...] Read more.
Analyzing the performance characteristics and applicable environments of intelligent vehicle lateral control algorithms, this paper establishes the Model Predictive Control (MPC), Pure Pursuit (PP), and Linear Quadratic Regulator (LQR) algorithms and uses 2022b MATLAB software to simulate the lateral error, heading error, and algorithm execution time at speeds of 3 m/s, 7 m/s, and 10 m/s. Urban low-speed scenarios (3 m/s) require high-precision control (such as obstacle avoidance), while high-speed scenarios (10 m/s) require strong stability. Existing research mostly focuses on a single speed and lacks a quantitative comparison across multiple operating conditions. Although MPC has high accuracy, its time consumption fluctuates greatly. LQR has strong real-time performance but a wide range of heading errors. PP has poor low-speed performance but controllable high-speed time consumption growth. It is necessary to define the applicable scenarios of each algorithm through quantitative data. In response to the lack of multi-speed domain quantitative comparison in existing research, this paper conducts multi-condition simulations using MPC, PP, and LQR algorithms and finds that at a low speed of 3 m/s, the peak lateral error of PP (0.45 m) is 55% and 156% higher than MPC (0.29 m) and LQR (0.176 m), respectively. At a speed of 10 m/s, the lateral error standard deviation of MPC (0.08 m) is reduced by 68% compared to PP (0.25 m). In terms of algorithm time consumption, LQR maintains full-speed domain stability (0.11–0.44 ms), while PP time increases by 95% with speed from 3 m/s to 10 m/s. The results show that in terms of lateral error, the MPC and LQR algorithms perform more stably, while the PP algorithm has a larger error at low speeds. Regarding heading error, all algorithms have a relatively large error range, but the MPC and LQR algorithms perform slightly better than the PP algorithm at high speeds. In terms of algorithm execution time, the LQR algorithm has the shortest and most stable execution time, the MPC algorithm has a relatively longer execution time, and the PP algorithm’s execution time varies at different speeds. Through this simulation, if high control accuracy and stability are pursued, the MPC or LQR algorithm can be considered; if real-time performance and computational efficiency are more important, the PP algorithm can be considered. Full article
(This article belongs to the Special Issue Dynamic Modeling, Identification, and Advanced Control of Intelligent Electric Vehicles)
►▼ Show Figures

Figure 1

19 pages, 6663 KiB  
Article
The Fault-Tolerant Control Strategy for the Steering System Failure of Four-Wheel Independent By-Wire Steering Electric Vehicles
by Qianlong Han, Chengye Liu, Jingbo Zhao and Haimei Liu
World Electr. Veh. J. 2025, 16(3), 183; https://doi.org/10.3390/wevj16030183 - 18 Mar 2025
Viewed by 343
Abstract
The drive torque of each wheel hub motor of a four-wheel independent wire-controlled steering electric vehicle is independently controllable, representing a typical over-actuated system. Through optimizing the distribution of the drive torque of each wheel, fault-tolerant control can be realized. In this paper, [...] Read more.
The drive torque of each wheel hub motor of a four-wheel independent wire-controlled steering electric vehicle is independently controllable, representing a typical over-actuated system. Through optimizing the distribution of the drive torque of each wheel, fault-tolerant control can be realized. In this paper, the four-wheel independent wire-controlled steering electric vehicle is taken as the research object, aiming at the collaborative control problem of trajectory tracking and yaw stability when the actuator of the by-wire steering system fails, a fault-tolerant control method based on the synergy of differential steering and direct yaw moment is proposed. This approach adopts a hierarchical control system. The front wheel controller predicts the necessary steering angle in accordance with a linear model and addresses the requirements of the front wheels and additional torque. Subsequently, considering the uncertainties in the drive control system and the complexities of the road obstacle model, the differential steering torque is computed via the sliding mode control method; the lower-level controller implements the torque optimization distribution strategy based on the quadratic programming algorithm. Finally, the validity of this approach under multiple working conditions was verified via CarSim 2019 and MATLAB R2023b/Simulink simulation experiments. Full article
(This article belongs to the Special Issue Dynamic Modeling, Identification, and Advanced Control of Intelligent Electric Vehicles)
►▼ Show Figures

Figure 1

16 pages, 3727 KiB  
Article
Anti-Lock Braking System Performance Optimization Based on Fitted-Curve Road-Surface Recognition and Sliding-Mode Variable-Structure Control
by Haiqing Zhou, Wenguang Liu, Ruochen Wang, Renkai Ding, Zhongyang Guo, Qing Ye, Xiangpeng Meng, Dong Sun and Wei Liu
World Electr. Veh. J. 2025, 16(3), 156; https://doi.org/10.3390/wevj16030156 - 6 Mar 2025
Viewed by 652
Abstract
This paper conducts an in-depth study on anti-lock braking technology in electronic hydraulic braking systems, focusing on a road-surface recognition algorithm based on fitted curves and a slip-rate control method based on sliding-mode variable structure. Firstly, a road-surface recognition algorithm using fitted curves [...] Read more.
This paper conducts an in-depth study on anti-lock braking technology in electronic hydraulic braking systems, focusing on a road-surface recognition algorithm based on fitted curves and a slip-rate control method based on sliding-mode variable structure. Firstly, a road-surface recognition algorithm using fitted curves is proposed, which extracts characteristic information by fitting the μ-λ curve, achieving the accurate identification of different road-surface conditions and providing optimal slip rates for subsequent braking control. Secondly, a slip-rate control strategy based on sliding-mode variable structure is designed to achieve optimal slip-rate control during vehicle braking, ensuring braking stability and safety under varying road conditions. Through theoretical analysis and simulation experiments, the results show that the proposed road-surface recognition algorithm can effectively identify various typical road surfaces (such as dry, wet, and icy/snowy surfaces) with high accuracy. The sliding-mode variable-structure control strategy can achieve good slip-rate control under different road conditions, effectively improving vehicle braking performance. This study provides an efficient and reliable technical solution for anti-lock braking control in electronic hydraulic braking systems, with significant theoretical and practical implications for enhancing vehicle braking safety. Full article
(This article belongs to the Special Issue Dynamic Modeling, Identification, and Advanced Control of Intelligent Electric Vehicles)
►▼ Show Figures

Figure 1

29 pages, 15007 KiB  
Article
Research on the A* Algorithm Based on Adaptive Weights and Heuristic Reward Values
by Xizheng Wang, Gang Li and Zijian Bian
World Electr. Veh. J. 2025, 16(3), 144; https://doi.org/10.3390/wevj16030144 - 4 Mar 2025
Viewed by 671
Abstract
Aiming at the problems of the A* algorithm’s long running time, large number of search nodes, tortuous paths, and the planned paths being prone to colliding with the corner points of obstacles, adaptive weighting and reward value theory are proposed to improve it. [...] Read more.
Aiming at the problems of the A* algorithm’s long running time, large number of search nodes, tortuous paths, and the planned paths being prone to colliding with the corner points of obstacles, adaptive weighting and reward value theory are proposed to improve it. Firstly, the diagonal-free five-way search based on the number of coordinate changes is used to make the algorithm purposeful. Meanwhile, in order to improve the path security, the diagonal search is filtered out when there are obstacles in the search neighborhood. Secondly, a radial basis function is used to act as the adaptive weighting coefficient of the heuristic function and adjust the proportion of heuristic functions in the algorithm accordingly to the search distance. Again, optimize the cost function using the reward value provided by the target point so that the current point is away from the local optimum. Finally, a secondary optimization of the path is performed to increase the distance between the path and the barriers, and the optimized path is smoothed using Bessel curves. Typical working conditions are selected, and the algorithm is verified through simulation tests. Simulation tests show that the algorithm not only shortens the planning time and improves the path security but also reduces the number of search nodes by about 76.4% on average and the turn angle by about 71.7% on average. Full article
(This article belongs to the Special Issue Dynamic Modeling, Identification, and Advanced Control of Intelligent Electric Vehicles)
►▼ Show Figures

Figure 1

17 pages, 1206 KiB  
Article
Multi-Criteria Analysis of Electric Vehicle Motor Technologies: A Review
by Emmanuel Kinoti, Thapelo C. Mosetlhe and Adedayo A. Yusuff
World Electr. Veh. J. 2024, 15(12), 541; https://doi.org/10.3390/wevj15120541 - 21 Nov 2024
Viewed by 2180
Abstract
The electric vehicle market is constantly evolving, with the research and development efforts to improve motor technologies and address the current challenges to meet the growing demand for sustainable transportation solutions well underway. Electric vehicles are crucial to the global initiative to reduce [...] Read more.
The electric vehicle market is constantly evolving, with the research and development efforts to improve motor technologies and address the current challenges to meet the growing demand for sustainable transportation solutions well underway. Electric vehicles are crucial to the global initiative to reduce carbon emissions. The core component of an electric vehicle is its motor drive technology, which has undergone significant advancements and diversification in recent years. Although alternating-current motors, particularly induction and synchronous motors, are widely used for their efficiency and low maintenance, direct-current motors provide high torque and cost-effectiveness advantages. This study examines various electric motor technologies used in electric vehicles and compares them using several parameters, such as reliability, cost, and efficiency. This study presents a multi-criteria comparison of the various electric motors used in the electric traction system to provide a picture that enables selecting the appropriate electrical motor for the intended application. Although the permanent magnet synchronous motor appears to be the popular choice among electric car makers, the proposed comparative study demonstrates that the induction motor matches the essential requirements of electric vehicles. Full article
(This article belongs to the Special Issue Dynamic Modeling, Identification, and Advanced Control of Intelligent Electric Vehicles)
►▼ Show Figures

Figure 1

18 pages, 5215 KiB  
Article
Cascaded Vehicle State Estimation Method of 4WIDEVs Considering System Delay and Noise
by Zibin Yang, Xiang Liu and Qiu Xia
World Electr. Veh. J. 2024, 15(10), 454; https://doi.org/10.3390/wevj15100454 - 7 Oct 2024
Viewed by 1136
Abstract
Considering the negative effects of time delay and noise on vehicle state estimation, a cascaded estimation means for the vehicle sideslip angle is proposed utilizing the ODUKF algorithm. To achieve strong-correlation decoupling between state variables and model interference of the EDWM, an augmented [...] Read more.
Considering the negative effects of time delay and noise on vehicle state estimation, a cascaded estimation means for the vehicle sideslip angle is proposed utilizing the ODUKF algorithm. To achieve strong-correlation decoupling between state variables and model interference of the EDWM, an augmented EDWM was constructed by introducing the tire relaxation length dynamic equation, which enables the precise model relationship between the longitudinal and transverse tire force relaxation length to be constructed while also achieving the decoupling of the system state from the unknown input. To achieve a vehicle driving state estimation, a hierarchical estimation architecture was adopted to design a cascading estimation method for the vehicle driving state. By using tire force estimation values as input for the vehicle driving state estimation, the required vehicle body postures can be estimated. At the same time, facing the problems of system delay and noise, an estimator derived from the ODUKF is designed by combining the model and cascade estimation strategy. The simulation comparative analysis and quantitative statistical results under multiple operating conditions provide evidence that the developed means effectively heighten the estimation accurateness and real-time performance while considering system time delay and noise. Full article
(This article belongs to the Special Issue Dynamic Modeling, Identification, and Advanced Control of Intelligent Electric Vehicles)
►▼ Show Figures

Figure 1

Review

Jump to: Research

36 pages, 7456 KiB  
Review
A Review of Research on Longitudinal Control of Intelligent Vehicles Based on Drive/Brake by Wire
by Peicheng Shi, Xinyu Qian, Chakir Chadia, Yu Sun, Taonian Liang and Aixi Yang
World Electr. Veh. J. 2024, 15(12), 557; https://doi.org/10.3390/wevj15120557 - 1 Dec 2024
Viewed by 2042
Abstract
In recent years, with the rapid innovation of science and technology, wire control technology, as a key technology, has achieved the transmission control of vehicles through the form of “electrical signals”, which has become an important foundation for realizing the high degree of [...] Read more.
In recent years, with the rapid innovation of science and technology, wire control technology, as a key technology, has achieved the transmission control of vehicles through the form of “electrical signals”, which has become an important foundation for realizing the high degree of intelligence of vehicles. This paper provides a comprehensive overview of the wire control technology, its application and longitudinal control strategy, and focuses on the longitudinal control technology of intelligent vehicles based on drive/brake by wire. The specific content includes five parts: first, the principles and characteristics of wire control technology and its application in intelligent vehicles are introduced; then, two commonly used longitudinal control strategies are described; then, the application of classical control technologies (such as PID, MPC, and sliding-mode control) in the longitudinal control of intelligent vehicles is discussed, including their working principles, characteristics and related research; subsequently, the AI control technology (deep reinforcement learning) is presented in the longitudinal control of intelligent vehicles, discussing its theoretical basis, the current status of algorithm research, control methods, and practical applications, etc.; finally, the paper summarizes the advantages and disadvantages of the classical control technology and AI control technology, and looks forward to the application and development prospects of these two control technologies in the control of intelligent vehicles. Full article
(This article belongs to the Special Issue Dynamic Modeling, Identification, and Advanced Control of Intelligent Electric Vehicles)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop