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Control Design for Electric Vehicles

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 13157

Special Issue Editor

Systems and Control Laboratory, Institute for Computer Science and Control, Kende u. 13-17, 1111 Budapest, Hungary
Interests: linear and nonlinear systems; robust and optimal control; integrated control; sensor fusion; system identification and identification for control; machine learning; mechanical systems; vehicle dynamics and vehicle control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

The recent research and development directions focus on exciting topics in the field of electrical and autonomous vehicles. This research enables to improve safety, energy efficiency, comfort and transport economy. The research tasks can be classified into several groups such as vehicle modeling, performance requirements, control of chassis elements and powertrain, and, moreover, route planning and trajectory design. The utilization of sensor fusion, actuator integration, V2X communication and cloud computing may also extend to both microscopic and macroscopic approaches in the field of traffic control.

The tools and methods for such developments range from classic and modern control theory, game-theoretical approaches, nonlinear programming, artificial intelligence, and machine learning. The new advantages of supervised and reinforcement learning also show high potential for individual and multi-agent control of electrical and autonomous vehicles.

In addition, road vehicles other components of transport systems are also included such as rail vehicles and ships, where simultaneous energy efficient and safe operation can be handled with similar tools and approaches. 

As the machine learning based approaches of self-driving vehicles require high computational resources and a large amount of data for testing and validation, the development of different frameworks, such as hardware-in-the-loop, simulations, virtual- and augmented reality should also be encouraged.

Potential topics include, but are not limited to, the following:

  • Self-driving vehicles, autonomous functions
  • Automatic train operation
  • V2X communications, sensor fusion
  • Actuator and control integrations
  • Energy-efficient control
  • Machine learning, reinforcement learning
  • Robust control, model predictive control
  • Testing and validation
  • Simulations, virtual- and augmented reality
  • Development frameworks, prototype constructions

Prof. Dr. Peter Gaspar
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • autonomous functions
  • robust and predictive control
  • machine learning
  • reinforcement learning
  • validation
  • prototype systems

Published Papers (5 papers)

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Editorial

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2 pages, 168 KiB  
Editorial
Control Design for Electric Vehicles
by Péter Gáspár
Energies 2022, 15(12), 4193; https://0-doi-org.brum.beds.ac.uk/10.3390/en15124193 - 07 Jun 2022
Viewed by 1103
Abstract
The recent research in the field of electrical and autonomous vehicles is developing in exciting directions [...] Full article
(This article belongs to the Special Issue Control Design for Electric Vehicles)

Research

Jump to: Editorial

17 pages, 1702 KiB  
Article
An LPV-Based Online Reconfigurable Adaptive Semi-Active Suspension Control with MR Damper
by Hakan Basargan, András Mihály, Péter Gáspár and Olivier Sename
Energies 2022, 15(10), 3648; https://0-doi-org.brum.beds.ac.uk/10.3390/en15103648 - 16 May 2022
Cited by 12 | Viewed by 1802
Abstract
This study introduces an online reconfigurable road-adaptive semi-active suspension controller that reaches the performance objectives with satisfying the dissipativity constraint. The concept of the model is based on a nonlinear static model of the semi-active Magnetorheological (MR) damper with considering the bi-viscous and [...] Read more.
This study introduces an online reconfigurable road-adaptive semi-active suspension controller that reaches the performance objectives with satisfying the dissipativity constraint. The concept of the model is based on a nonlinear static model of the semi-active Magnetorheological (MR) damper with considering the bi-viscous and hysteretic behaviors of the damper. The input saturation problem has been solved by using the proposed method in the literature that allows the integration of the saturation actuator in the initial system to create a Linear Parameter Varying (LPV) system. The control input meets the saturation constraint; therewith, the dissipativity constraint is fulfilled. The online reconfiguration and adaptivity problem is solved by using an external scheduling variable that allows the trade-off between driving comfort and road holding/stability. The control design is based on the LPV framework. The proposed adaptive semi-active suspension controller is compared to passive suspension and Bingham model with Simulink simulation, and then the adaptivity of the controller is validated with the TruckSim environment. The results show that the proposed LPV controller has better performance results than the controlled Bingham and passive semi-active suspension model. Full article
(This article belongs to the Special Issue Control Design for Electric Vehicles)
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24 pages, 5304 KiB  
Article
Development of Online Adaptive Traction Control for Electric Robotic Tractors
by Idris Idris Sunusi, Jun Zhou, Chenyang Sun, Zhenzhen Wang, Jianlei Zhao and Yongshuan Wu
Energies 2021, 14(12), 3394; https://0-doi-org.brum.beds.ac.uk/10.3390/en14123394 - 09 Jun 2021
Cited by 5 | Viewed by 2611
Abstract
Estimation and control of wheel slip is a critical consideration in preventing loss of traction, minimizing power consumptions, and reducing soil disturbance. An approach to wheel slip estimation and control, which is robust to sensor noises and modeling imperfection, has been investigated in [...] Read more.
Estimation and control of wheel slip is a critical consideration in preventing loss of traction, minimizing power consumptions, and reducing soil disturbance. An approach to wheel slip estimation and control, which is robust to sensor noises and modeling imperfection, has been investigated in this study. The proposed method uses a simplified form of wheels longitudinal dynamic and the measurement of wheel and vehicle speeds to estimate and control the optimum slip. The longitudinal wheel forces were estimated using a robust sliding mode observer. A straightforward and simple interpolation method, which involves the use of Burckhardt tire model, instantaneous values of wheel slip, and the estimate of longitudinal force, was used to determine the optimum slip ratio that guarantees maximum friction coefficient between the wheel and the road surface. An integral sliding mode control strategy was also developed to force the wheel slip to track the desired optimum value. The algorithm was tested in Matlab/Simulink environment and later implemented on an autonomous electric vehicle test platform developed by the Nanjing agricultural university. Results from simulation and field tests on surfaces with different friction coefficients (μ) have proved that the algorithm can detect an abrupt change in terrain friction coefficient; it can also estimate and track the optimum slip. More so, the result has shown that the algorithm is robust to bounded variations on the weight on the wheels and rolling resistance. During simulation and field test, the system reduced the slip from non-optimal values of about 0.8 to optimal values of less than 0.2. The algorithm achieved a reduction in slip ratio by reducing the torque delivery to the wheel, which invariably leads to a reduction in wheel velocity. Full article
(This article belongs to the Special Issue Control Design for Electric Vehicles)
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15 pages, 1483 KiB  
Article
Design Framework for Achieving Guarantees with Learning-Based Observers
by Balázs Németh, Tamás Hegedűs and Péter Gáspár
Energies 2021, 14(8), 2039; https://0-doi-org.brum.beds.ac.uk/10.3390/en14082039 - 07 Apr 2021
Cited by 4 | Viewed by 1451
Abstract
The paper proposes a novel framework for state observer design, in which learning-based observers are incorporated. The aim of the method is to provide a framework, which is able to guarantee the limitation of the observation error, even if the error of the [...] Read more.
The paper proposes a novel framework for state observer design, in which learning-based observers are incorporated. The aim of the method is to provide a framework, which is able to guarantee the limitation of the observation error, even if the error of the learning-based observer under all scenarios cannot be verified. The framework is based on the robust H design method, which is able to provide guarantees on the resulted observer. Moreover, the observer design process is extended with a controller design, which leads to a joint robust H controller-observer design. In this paper the proposed method is applied on a vehicle control problem, such as lateral path following. In this problem the goal of the observer is to provide an accurate lateral velocity signal for the vehicle, which is used in the controlled system for the generation of front wheel steering angle. The effectiveness of the method is illustrated through simulation examples on high-fidelity vehicle dynamic simulator CarMaker. Full article
(This article belongs to the Special Issue Control Design for Electric Vehicles)
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16 pages, 4796 KiB  
Article
A Novel Data-Driven Modeling and Control Design Method for Autonomous Vehicles
by Dániel Fényes, Balázs Németh and Péter Gáspár
Energies 2021, 14(2), 517; https://0-doi-org.brum.beds.ac.uk/10.3390/en14020517 - 19 Jan 2021
Cited by 16 | Viewed by 4213
Abstract
This paper presents a novel modeling method for the control design of autonomous vehicle systems. The goal of the method is to provide a control-oriented model in a predefined Linear Parameter Varying (LPV) structure. The scheduling variables of the LPV model [...] Read more.
This paper presents a novel modeling method for the control design of autonomous vehicle systems. The goal of the method is to provide a control-oriented model in a predefined Linear Parameter Varying (LPV) structure. The scheduling variables of the LPV model through machine-learning-based methods using a big dataset are selected. Moreover, the LPV model parameters through an optimization algorithm are computed, with which accurate fitting on the dataset is achieved. The proposed method is illustrated on the nonlinear modeling of the lateral vehicle dynamics. The resulting LPV-based vehicle model is used for the control design of path following functionality of autonomous vehicles. The effectiveness of the modeling and control design methods through comprehensive simulation examples based on a high-fidelity simulation software are illustrated. Full article
(This article belongs to the Special Issue Control Design for Electric Vehicles)
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