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
Intelligent Electric Vehicle Control, Testing and Evaluation
share announcement

Intelligent Electric Vehicle Control, Testing and Evaluation

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 10547

Special Issue Editors

Dr. Yong Li

E-Mail Website
Guest Editor
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, China
Interests: motor control and thermal management; vehicle dynamics and control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hongyu Zheng

E-Mail Website
Guest Editor
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China
Interests: dynamic simulation and control of automotive intelligent chassis
Prof. Dr. Tianjun Zhu

E-Mail Website
Guest Editor
Department of Mechanical and Automotive Engineering, Zhaoqing University, Zhaoqing, China
Interests: autonomous driving decision control; vehicle dynamics control
Prof. Dr. Zhifu Wang

E-Mail Website
Guest Editor
School of Mechanical Engineering and the National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing, China
Interests: motor driver; design of power systems for vehicles
Dr. Hongliang Wang

E-Mail Website
Guest Editor
Safety and Intelligence of Vehicle Equipment Research Center, Nanjing University of Science and Technology, Nanjing, China
Interests: intelligent chassis; unmanned vehicles

Special Issue Information

Dear Colleagues,

Electrification, intelligence, and networkization have become the three development directions of the global automobile industry. Intelligent electric vehicles integrate a variety of transformative technologies, such as artificial intelligence, big data, and a new generation of communication and information technology, covering automatic control, computer vision, sensor fusion, vehicle engineering, and other disciplines. Vehicles are gradually evolving from simple transportation to intelligent mobile terminals. Intelligent networked electric vehicles can not only provide different functions and services, but also bring disruptive changes to vehicle design. Both academia and industry have carried out a large amount of research in the fields of vehicle environment perception and decision, trajectory planning and tracking, algorithm, vehicle dynamics control, electric drive assembly design and control, vehicle thermal management and energy management, vehicle chassis design and control, etc. However, there is still a huge unexplored space in the new configuration design and new function realization of intelligent electric vehicles.

Dr. Yong Li
Prof. Dr. Hongyu Zheng
Prof. Dr. Tianjun Zhu
Prof. Dr. Zhifu Wang
Dr. Hongliang Wang
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 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

  • intelligent unmanned vehicle tracking technology
  • obstacle detection and high-precision map construction technology
  • unmanned vehicle dynamics control technology
  • multi-sensor information perception and fusion technology
  • intelligent vehicle decision and control technology
  • intelligent wire chassis development and collaborative control technology
  • advanced electric drive system design and control technology
  • intelligent vehicle energy management technology
  • intelligent vehicle thermal management technology

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 polices can be found here.

Published Papers (10 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

20 pages, 11953 KiB  
Article
Direct Power Control of Vienna Rectifier Based on Fractional Order Sliding Mode Control
by Tao Wang, Shenhui Chen, Xin Li, Jihui Zhang and Jinghao Ma
World Electr. Veh. J. 2024, 15(12), 543; https://doi.org/10.3390/wevj15120543 - 22 Nov 2024
Viewed by 314
Abstract
Taking a Vienna rectifier as the research object, the power mathematical model based on a switching function is established according to its working principle. A sliding mode variable structure control algorithm based on the reaching law is examined in order to address the [...] Read more.
Taking a Vienna rectifier as the research object, the power mathematical model based on a switching function is established according to its working principle. A sliding mode variable structure control algorithm based on the reaching law is examined in order to address the issues of the slow response speed and inadequate anti-interference of classical PI control in the face of abrupt changes in the DC-side load. In response to the sluggish convergence rate and inadequate chattering suppression of classical integer order sliding mode control, a fractional order exponential reaching law sliding mode, direct power control approach with rapid convergence is developed. The fractional calculus is introduced into the sliding mode control, and the dynamic performance and convergence speed of the control system are improved by increasing the degree of freedom of the fractional calculus operator. The method of including a balance factor in the zero-sequence component is employed to address the issue of the midpoint potential equilibrium in the Vienna rectifier. Ultimately, the suggested control is evaluated against classical PI control through simulation analysis and experimental validation. The findings indicate that the proposed technique exhibits rapid convergence, reduced control duration, and enhanced robustness, hence augmenting its resistance to interference. Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
►▼ Show Figures

Figure 1

25 pages, 11758 KiB  
Article
Research on the Smooth Switching Control Strategy of Electric Vehicle Charging Stations Based on Photovoltaic–Storage–Charging Integration
by Tao Wang, Jinghao Ma, Cunhao Lin, Xin Li, Shenhui Chen and Jihui Zhang
World Electr. Veh. J. 2024, 15(11), 528; https://doi.org/10.3390/wevj15110528 - 17 Nov 2024
Viewed by 461
Abstract
To facilitate seamless transitions between grid-connected and islanded modes in PV–storage–charging integration, an energy storage system converter is designated as the subject of investigation, and its operational principles are examined. Feed-forward decoupling, double closed-loop, constant-power (PQ), constant-voltage–constant-frequency (V/F), and constant-voltage charge and discharge [...] Read more.
To facilitate seamless transitions between grid-connected and islanded modes in PV–storage–charging integration, an energy storage system converter is designated as the subject of investigation, and its operational principles are examined. Feed-forward decoupling, double closed-loop, constant-power (PQ), constant-voltage–constant-frequency (V/F), and constant-voltage charge and discharge control strategies are developed. The PQ and V/F control framework of the energy storage battery comprises an enhanced common current inner loop and a switching voltage outer loop. The current reference value output by the voltage outer loop and the voltage signal output by the current inner loop are compensated. The transient impact is reduced, and the smooth switching of the microgrid from the grid-connected mode to the island mode is realized, which significantly improves the power quality and ensures the uninterrupted charging of electric vehicles and the stable operation of the key load of the system. By constructing a simulation model of the photovoltaic energy storage microgrid on the MATLAB/Simulink platform, the practicability of the control strategy proposed in this paper is verified. Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
►▼ Show Figures

Figure 1

22 pages, 9548 KiB  
Article
Research on the Synchronization Control Strategy of Regenerative Braking of Distributed Drive Electric Vehicles
by Ren He and Yukun Xie
World Electr. Veh. J. 2024, 15(11), 512; https://doi.org/10.3390/wevj15110512 - 7 Nov 2024
Viewed by 522
Abstract
To solve the problem of asynchronous speed between the coaxial in-wheel motors of distributed drive electric vehicle caused by changes in the road surface, load, and other factors during the regenerative braking of the vehicle, which may result in a yaw motion of [...] Read more.
To solve the problem of asynchronous speed between the coaxial in-wheel motors of distributed drive electric vehicle caused by changes in the road surface, load, and other factors during the regenerative braking of the vehicle, which may result in a yaw motion of the vehicle and a reduction in vehicle stability, a synchronization control strategy of regenerative braking for distributed drive electric vehicles is proposed. Firstly, a ring-coupled synchronous control strategy with the current compensation module is designed. Then, the speed controller of a permanent magnet synchronous in-wheel motor and a compensation controller of synchronous control are designed based on the non-singular fast terminal sliding mode control. Combining this with the regenerative braking control strategy, a regenerative braking synchronization control strategy is designed. The simulation results show that compared with the existing synchronization control strategy, the designed new ring-coupled synchronization control strategy can improve the speed synchronization performance between the motors after the disturbance. Moreover, compared with the conventional regenerative braking control strategy, the regenerative braking synchronization control strategy can reduce the speed synchronization error between the motors during the regenerative braking process, so as to improve the synchronization and output stability of the motors during the braking process. Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
►▼ Show Figures

Figure 1

23 pages, 7845 KiB  
Article
Estimation of Lithium-Ion Battery SOC Based on IFFRLS-IMMUKF
by Xianguang Zhao, Tao Wang, Li Li and Yanqing Cheng
World Electr. Veh. J. 2024, 15(11), 494; https://doi.org/10.3390/wevj15110494 - 29 Oct 2024
Viewed by 712
Abstract
The state of charge (SOC) is a characteristic parameter that indicates the remaining capacity of electric vehicle batteries. It plays a significant role in determining driving range, ensuring operational safety, and extending the service life of battery energy storage systems. Accurate SOC estimation [...] Read more.
The state of charge (SOC) is a characteristic parameter that indicates the remaining capacity of electric vehicle batteries. It plays a significant role in determining driving range, ensuring operational safety, and extending the service life of battery energy storage systems. Accurate SOC estimation can ensure the safety and reliability of vehicles. To tackle the challenge of precise SOC estimation in complex environments, this study introduces an improved forgetting factor recursive least squares (IFFRLS) method, which integrates the Golden Jackal optimization (GJO) algorithm with the traditional FFRLS method. This integration is grounded in the formulation of a lithium battery state equation derived from a second-order RC equivalent circuit model. Additionally, the research utilizes the interactive multiple model unscented Kalman filter (IMMUKF) algorithm for SOC estimation, with experimental validation conducted under various conditions, including hybrid pulse power characterization (HPPC), urban dynamometer driving schedule (UDDS), and real underwater scenarios. The experimental results demonstrate that the SOC estimation method of lithium batteries based on IFFRLS-IMMUKF exhibits high accuracy and a favorable temperature applicability range. Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
►▼ Show Figures

Figure 1

25 pages, 6995 KiB  
Article
The Control Strategies for Charging and Discharging of Electric Vehicles in the Vehicle–Grid Interaction Modes
by Tao Wang, Jihui Zhang, Xin Li, Shenhui Chen, Jinhao Ma and Honglin Han
World Electr. Veh. J. 2024, 15(10), 468; https://doi.org/10.3390/wevj15100468 - 14 Oct 2024
Cited by 2 | Viewed by 850
Abstract
In response to the challenges posed by large-scale, uncoordinated electric vehicle charging on the power grid, Vehicle-to-Grid (V2G) technology has been developed. This technology seeks to synchronize electric vehicles with the power grid, improving the stability of their connections and fostering positive energy [...] Read more.
In response to the challenges posed by large-scale, uncoordinated electric vehicle charging on the power grid, Vehicle-to-Grid (V2G) technology has been developed. This technology seeks to synchronize electric vehicles with the power grid, improving the stability of their connections and fostering positive energy exchanges between them. The key component for implementing V2G technology is the bidirectional AC/DC converter. This study concentrates on the non-isolated bidirectional AC/DC converter, providing a detailed analysis of its two-stage operation and creating a mathematical model. A dual closed-loop control structure for voltage and current is designed based on nonlinear control theory, along with a constant current charge–discharge control strategy. Furthermore, midpoint potential balance is achieved through zero-sequence voltage injection control, and power signals for the switching devices are generated using Space Vector Pulse Width Modulation (SVPWM) technology. A simulation model of the V2G system is then constructed in MATLAB/Simulink for analysis and validation. The findings demonstrate that the control strategy proposed in this paper improves the system’s robustness, dynamic performance, and resistance to interference, thus reducing the effects of large-scale, uncoordinated electric vehicle charging on the power grid. Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
►▼ Show Figures

Figure 1

17 pages, 5756 KiB  
Article
Physics-Informed Neural Network-Based Nonlinear Model Predictive Control for Automated Guided Vehicle Trajectory Tracking
by Yinping Li and Li Liu
World Electr. Veh. J. 2024, 15(10), 460; https://doi.org/10.3390/wevj15100460 - 10 Oct 2024
Viewed by 1535
Abstract
This paper proposes a nonlinear Model Predictive Control (MPC) method based on Physics-Informed Neural Networks (PINNs), aimed at enhancing the trajectory tracking performance of Automated Guided Vehicles (AGVs) in complex dynamic environments. Traditional physical models often face the challenges of computational inefficiency and [...] Read more.
This paper proposes a nonlinear Model Predictive Control (MPC) method based on Physics-Informed Neural Networks (PINNs), aimed at enhancing the trajectory tracking performance of Automated Guided Vehicles (AGVs) in complex dynamic environments. Traditional physical models often face the challenges of computational inefficiency and insufficient control precision when dealing with complex dynamic systems. However, by integrating physical laws directly into the training process of neural networks, PINNs can effectively learn and capture the kinematic characteristics of vehicles, replacing traditional nonlinear ordinary differential equation models and thus significantly enhancing computational efficiency and control performance. During the model-training phase, this study further incorporates the Theory of Functional Connections (TFC) and adaptive loss balancing strategies to efficiently solve ODE problems without relying on numerical integration and optimize the control strategy. This combined approach not only reduces computational complexity, but also improves the robustness and precision of the control strategy in varying environments. Numerical simulations demonstrate that this method offers significant advantages in AGV trajectory-tracking tasks, manifested in higher computational efficiency and precise control performance. The proposal of the PINN-MPC method provides new theoretical support and innovative methods for real-time complex system control, with important research and application potential, and is expected to play a key role in future intelligent control systems. Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
►▼ Show Figures

Figure 1

18 pages, 8254 KiB  
Article
Fractional Sliding Mode Observer Control Strategy for Three-Phase PWM Rectifier
by Tao Wang, Xin Li, Jihui Zhang, Shenhui Chen, Jinghao Ma and Cunhao Lin
World Electr. Veh. J. 2024, 15(7), 316; https://doi.org/10.3390/wevj15070316 - 18 Jul 2024
Cited by 1 | Viewed by 903
Abstract
This research presents a novel current loop control strategy for a three-phase PWM rectifier system aimed at mitigating challenges related to substandard power quality, excessive current harmonics, and insufficient robustness. The suggested approach combines an extended state observer (ESO) with dual-power sliding mode [...] Read more.
This research presents a novel current loop control strategy for a three-phase PWM rectifier system aimed at mitigating challenges related to substandard power quality, excessive current harmonics, and insufficient robustness. The suggested approach combines an extended state observer (ESO) with dual-power sliding mode control that is further enhanced by fractional-order micro-integral operators. This amalgamation enhances the adaptability of the controller to system dynamics and augments the flexibility of the current loop control mechanism. The results of this integration include diminished system oscillations, heightened immunity to external disturbances, and improved robustness and dynamics of the overall system. Through MATLAB/Simulink simulations, the effectiveness of the proposed control methodology is validated, demonstrating superior performance in terms of robustness, dynamic response, power quality enhancement, and mitigation of current harmonics when compared to conventional PI control and standard fractional-order dual-power sliding mode control techniques. Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
►▼ Show Figures

Figure 1

19 pages, 7279 KiB  
Article
Decoupled Adaptive Motion Control for Unmanned Tracked Vehicles in the Leader-Following Task
by Jingjing Fan, Pengxiang Yan, Ren Li, Yi Liu, Falong Wang, Yingzhe Liu and Chang Chen
World Electr. Veh. J. 2024, 15(6), 239; https://doi.org/10.3390/wevj15060239 - 30 May 2024
Viewed by 832
Abstract
As a specific task for unmanned tracked vehicles, leader-following imposes high-precision requirements on the vehicle’s motion control, especially the steering control. However, due to characteristics such as the frequent changes in off-road terrain and steering resistance coefficients, controlling tracked vehicles poses significant challenges, [...] Read more.
As a specific task for unmanned tracked vehicles, leader-following imposes high-precision requirements on the vehicle’s motion control, especially the steering control. However, due to characteristics such as the frequent changes in off-road terrain and steering resistance coefficients, controlling tracked vehicles poses significant challenges, making it difficult to achieve stable and precise leader-following. This paper decouples the leader-following control into speed and curvature control to address such issues. It utilizes model reference adaptive control to establish reference models for the speed and curvature subsystems and designs corresponding parameter adaptive control laws. This control method enables the actual vehicle speed and curvature to effectively track the response of the reference model, thereby addressing the impact of frequent changes in the steering resistance coefficient. Furthermore, this paper demonstrates significant improvements in leader-following performance through a series of simulations and experiments. Compared with the traditional PID control method, the results shows that the maximum following distance has been reduced by at least approximately 12% (ensuring the ability to keep up with the leader), the braking distance has effectively decreased by 22% (ensuring a safe distance in an emergency braking scenario and improving energy recovery), the curvature tracking accuracy has improved by at least 11% (improving steering performance), and the speed tracking accuracy has increased by at least 3.5% (improving following performance). Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
►▼ Show Figures

Figure 1

15 pages, 6034 KiB  
Article
Distributed-Drive Vehicle Lateral-Stability Coordinated Control Based on Phase-Plane Stability Region
by Jun Liu and Ang Dai
World Electr. Veh. J. 2024, 15(5), 202; https://doi.org/10.3390/wevj15050202 - 7 May 2024
Cited by 1 | Viewed by 1079
Abstract
The lateral stability control of vehicles is one of the most crucial aspects of vehicle safety. This article introduces a coordinated-control strategy designed to enhance the handling stability of distributed-drive electric vehicles. The upper controller uses active front steering and direct yaw moment-control [...] Read more.
The lateral stability control of vehicles is one of the most crucial aspects of vehicle safety. This article introduces a coordinated-control strategy designed to enhance the handling stability of distributed-drive electric vehicles. The upper controller uses active front steering and direct yaw moment-control controllers designed based on sliding-mode control theory. The lower controller optimally allocates control inputs to the upper controller, considering factors such as load transfer and tire load rate. It divides the stability region by relying on the phase plane and develops a coordinated-control strategy based on the degree of deviation of the vehicle state from the stability region. The results of the simulation experiments demonstrate that the proposed control strategy effectively improves handling stability under extreme working conditions. Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
►▼ Show Figures

Figure 1

18 pages, 4891 KiB  
Article
Integrated Path Following and Lateral Stability Control of Distributed Drive Autonomous Unmanned Vehicle
by Feng Zhao, Jiexin An, Qiang Chen and Yong Li
World Electr. Veh. J. 2024, 15(3), 122; https://doi.org/10.3390/wevj15030122 - 21 Mar 2024
Cited by 4 | Viewed by 1897
Abstract
Intelligentization is the development trend of the future automobile industry. Intelligentization requires that the dynamic control of the vehicle can complete the trajectory tracking according to the trajectory output of the decision planning the driving state of the vehicle and ensure the driving [...] Read more.
Intelligentization is the development trend of the future automobile industry. Intelligentization requires that the dynamic control of the vehicle can complete the trajectory tracking according to the trajectory output of the decision planning the driving state of the vehicle and ensure the driving safety and stability of the vehicle. However, trajectory limit planning and harsh road conditions caused by emergencies will increase the difficulty of trajectory tracking and stability control of unmanned vehicles. In view of the above problems, this paper studies the trajectory tracking and stability control of distributed drive unmanned vehicles. This paper applies a hierarchical control framework. Firstly, in the upper controller, an adaptive prediction time linear quadratic regulator (APT LQR) path following algorithm is proposed to acquire the desired front-wheel-steering angle considering the dynamic stability performance of the tires. The lateral stability of the DDAUV is determined based on the phase plane, and the sliding surface, in the improved sliding mode control (SMC), is further dynamically adjusted to obtain the desired additional yaw moment for coordinating the path following and lateral stability. Then, in the lower controller, considering the slip and the working load of four tires, a comprehensive cost function is established to reasonably distribute the driving torque of four in-wheel motors (IWMs) for producing the desired additional yaw moment. Finally, the proposed control algorithm is verified by the hardware-in-the-loop (HIL) experiment platform. The results show the path following and lateral stability can be coordinated effectively under different driving conditions. Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-10
Back to TopTop