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Advanced Vehicle Dynamics Identification, Control and Observer Methods for Autonomous,...
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Advanced Vehicle Dynamics Identification, Control and Observer Methods for Autonomous, Electrified Vehicles

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 5312

Special Issue Editors

Dr. Tamás Hegedűs

E-Mail Website
Guest Editor
Systems and Control Laboratory, Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), H-1111 Budapest, Hungary
Interests: autonomous vehicle; ultra-local model-based control; overtaking strategies
Dr. Daniel Fenyes

E-Mail Website
Guest Editor
Systems and Control Laboratory, Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), H-1111 Budapest, Hungary
Interests: vehicle dynamics; robust control; machine learning-based control; state estimation

Special Issue Information

Dear Colleagues,

Nowadays, the main focus of the automotive industry is on the development of fully automated, electrified vehicles. This task poses several challenges, which must be solved before launching the first self-driving vehicle. These challenges can be divided into three main groups:

  1. Identification of vehicle dynamics, which aims to provide a reliable model of the vehicle.
  2. Observer design, whose goal is to estimate the unmeasurable states of the vehicle and its battery system.
  3. Control design, which guarantees the stable and precise motion of the vehicle and maximizes the operation range of the battery system through the optimization of the velocity profile of the vehicle.

Although there are some solutions in the literature, these topics still have some open questions. The goal of this special issue is to provide a platform for research, which addresses one of the mentioned issues.

Dr. Tamás Hegedűs
Dr. Daniel Fenyes
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

  • autonomous electrified vehicles
  • control design
  • observer design
  • state-estimation
  • vehicle dynamics
  • connected vehicles
  • robust methods
  • machine learning methods

Published Papers (3 papers)

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Research

11 pages, 1299 KiB  
Article
Vehicle Dynamics in Electric Cars Development Using MSC Adams and Artificial Neural Network
by Santiago J. Cachumba-Suquillo, Mariel Alfaro-Ponce, Sergio G. Torres-Cedillo, Jacinto Cortés-Pérez and Moises Jimenez-Martinez
World Electr. Veh. J. 2023, 14(10), 293; https://doi.org/10.3390/wevj14100293 - 15 Oct 2023
Viewed by 2106
Abstract
Recently, there has been renewed interest in lightweight structures; however, a small structure change can strongly affect vehicle dynamic behavior. Therefore, this study provides new insights into non-parametric modeling based on artificial neural networks (ANNs). This work is then motivated by the requirement [...] Read more.
Recently, there has been renewed interest in lightweight structures; however, a small structure change can strongly affect vehicle dynamic behavior. Therefore, this study provides new insights into non-parametric modeling based on artificial neural networks (ANNs). This work is then motivated by the requirement for a reliable substitute for virtual instrumentation in electric car development to enable the prediction of the current value of the vehicle slip from a given time history of the vehicle (input) and previous values of synthetic data (feedback). The training data are generated from a multi-body simulation using MSC Adams Car; the simulation involves a double lane-change maneuver. This test is commonly used to evaluate vehicle stability. Based on dynamic considerations, this study implements the nonlinear autoregressive exogenous (NARX) identification scheme used in time-series modeling. This work presents an ANN that is able to predict the side slip angle from simulated training data generated employing MSC Adams Car. This work is a specific solution to overtake maneuvers, avoiding the loss of vehicle control and increasing driving safety. Full article
(This article belongs to the Special Issue Advanced Vehicle Dynamics Identification, Control and Observer Methods for Autonomous, Electrified Vehicles)
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17 pages, 3324 KiB  
Article
Active Control for an Electric Vehicle with an Observer for Torque Energy-Saving
by Juan Miguel González-López, Sergio Sandoval Pérez, Ramón O. Jiménez Betancourt and Gilberto Barreto
World Electr. Veh. J. 2023, 14(10), 288; https://doi.org/10.3390/wevj14100288 - 10 Oct 2023
Viewed by 1322
Abstract
Vehicle dynamics play an important role in determining a vehicle’s stability. It is necessary to identify and obtain models related to vehicle dynamics to evaluate the performance of electric vehicles, as well as how to control them. This paper presents fundamentals of vehicle [...] Read more.
Vehicle dynamics play an important role in determining a vehicle’s stability. It is necessary to identify and obtain models related to vehicle dynamics to evaluate the performance of electric vehicles, as well as how to control them. This paper presents fundamentals of vehicle dynamics, proposing a three-degree-of-freedom nonlinear observer and controller to control lateral velocity and tire torque in comparison to a PID control, while also utilizing a Lyapunov function to determine the stability of the controlled state feedback system concerning the observer, which estimates state errors. This work demonstrates the mathematical development of estimations that will be fed into the algorithms of two active nonlinear controls (state feedback and PID), utilizing the results from Matlab-Simulink simulations of tire torque, lateral and angular velocities based on longitudinal velocity measurements, and employing dynamic gains, such as response to a steering maneuver by the driver following the international standards ISO 7401/2011 and ISO 3888-2. It is concluded that the observer is robust and exhibits energy-saving efficiency in tire torque, even under conditions of variable tire-ground friction. Full article
(This article belongs to the Special Issue Advanced Vehicle Dynamics Identification, Control and Observer Methods for Autonomous, Electrified Vehicles)
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14 pages, 3473 KiB  
Article
An FPGA-Based Hardware Low-Cost, Low-Consumption Target-Recognition and Sorting System
by Yulu Wang, Yi Han, Jun Chen, Zhou Wang and Yi Zhong
World Electr. Veh. J. 2023, 14(9), 245; https://doi.org/10.3390/wevj14090245 - 04 Sep 2023
Cited by 1 | Viewed by 1450
Abstract
In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in industrial item-sorting systems and proposes an object-recognition and sorting system for autonomous driving. In industrial sorting lines, goods-sorting robots [...] Read more.
In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in industrial item-sorting systems and proposes an object-recognition and sorting system for autonomous driving. In industrial sorting lines, goods-sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot’s real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consumption. These issues mean that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array)-based item-sorting system that achieves similar performance to current mainstream sorting systems but at a lower cost and consumption. The recognition component employs a morphological-recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degrees-of-freedom robotic arm is introduced into the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26 ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs. Full article
(This article belongs to the Special Issue Advanced Vehicle Dynamics Identification, Control and Observer Methods for Autonomous, Electrified Vehicles)
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