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Advances in Control for Electric Vehicle
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Advances in Control for Electric Vehicle

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 23259

Special Issue Editors

Dr. Seongjin Yim

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Guest Editor
Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 139-743, Republic of Korea
Interests: vehicle dynamics and control; state and parameter estimation; steer-by-wire; integrated chassis control with V2X communication
Special Issues, Collections and Topics in MDPI journals
Dr. Yafei Wang

E-Mail Website
Guest Editor
Institute of Intelligent Vehicle, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
Interests: vehicle dynamics and control; connected and automated vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Kanghyun Nam

E-Mail Website
Guest Editor
School of Mechanical Engineering, Yeungnam University, Gyeongsan 38541, Gyeongbuk, Republic of Korea
Interests: vehicle dynamics and motion control; in-wheel-motor EV; steer-by-wire system control; traction control and stability control of EVs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

You are invited to submit papers to a Special Issue of Electronics on “Advances in Control for Electric Vehicles”. Recent trends in automotive engineering point out that the influence of an electric powertrain, especially in the case of individual on-board and in-wheel motors, on electric vehicle dynamics in terms of stability, handling, agility, and comfort should be carefully considered on various development and design stages. On the other hand, on-board and in-wheel motors can not only be used in traditional vehicle dynamics control systems such as ABS or ESC but can also cause emerging new motion control functions through the blended operation of various powertrain and chassis actuators. All these factors should be properly considered in studies on vehicle dynamics and motion control system design for electric vehicles.

Prof. Seongjin Yim
Dr. Yafei Wang
Prof. Dr. Kanghyun Nam
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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 and motion control for EVs
  • State and parameter estimation for EVs
  • Trajectory planning for automated EVs
  • Coordinate control for connected and automated EVs
  • Anti-slip control and traction control of EVs
  • Advanced steering control of EVs
  • Battery state monitoring and control of EVs
  • Energy-saving technologies for EVs
  • In-wheel-motor EVs
  • Fault-tolerant control for EVs

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Published Papers (6 papers)

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Research

20 pages, 7586 KiB  
Article
Intelligent Vehicle Trajectory Tracking Control Based on VFF-RLS Road Friction Coefficient Estimation
by Yanxin Nie, Yiding Hua, Minglu Zhang and Xiaojun Zhang
Electronics 2022, 11(19), 3119; https://doi.org/10.3390/electronics11193119 - 29 Sep 2022
Cited by 4 | Viewed by 1931
Abstract
This paper proposes an autonomous vehicle trajectory tracking system that fully considers road friction. When an intelligent vehicle drives at high speed on roads with different friction coefficients, the difficulty of its trajectory tracking control lies in the fast and accurate identification of [...] Read more.
This paper proposes an autonomous vehicle trajectory tracking system that fully considers road friction. When an intelligent vehicle drives at high speed on roads with different friction coefficients, the difficulty of its trajectory tracking control lies in the fast and accurate identification of road friction coefficients. Therefore, an improved strategy is designed based on traditional recursive least squares (RLS), which is utilized for accurate identification of the friction coefficient. First, the tire force and slip rate required for the estimation of the road friction coefficient by constructing the vehicle dynamics model and tire effective model are calculated. In this paper, a variable forgetting factor recursive least squares (VFF-RLS) method is proposed for the construction of the friction coefficient estimator. Second, the identified results are output to the model predictive controller (MPC) constructed in this paper as a way to improve tire slip angle constraints, to realize the trajectory tracking of the intelligent vehicle. Finally, the joint simulation test results of Carsim and Matlab/Simulink show that the trajectory tracking system based on the VFF-RLS friction coefficient estimator has outstanding tracking performance. Full article
(This article belongs to the Special Issue Advances in Control for Electric Vehicle)
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14 pages, 2812 KiB  
Article
Dynamic Analysis of a Planar Suspension Mechanism Based on Kinestatic Relations
by Guofeng Zhou, Shengye Jin, Yafei Wang, Zhisong Zhou and Shouqi Cao
Electronics 2022, 11(12), 1856; https://doi.org/10.3390/electronics11121856 - 11 Jun 2022
Cited by 1 | Viewed by 2226
Abstract
The dynamic characteristics of a vehicle are significantly influenced by the suspension mechanism. In this paper, the nonlinear kinestatic relations of a planar suspension mechanism are taken into account in the dynamic analysis of a vehicle. A planar suspension mechanism can be considered [...] Read more.
The dynamic characteristics of a vehicle are significantly influenced by the suspension mechanism. In this paper, the nonlinear kinestatic relations of a planar suspension mechanism are taken into account in the dynamic analysis of a vehicle. A planar suspension mechanism can be considered a 1-DOF parallel mechanism. The Jacobian is used for the kinestatic analysis of the suspension. The motions of the suspension can be represented by instantaneous screw. Based on these kinematic and static relations, the dynamic performances of a quarter-vehicle model with a planar suspension mechanism are described in terms of Lagrangian equations. Finally, as illustrated in the examples, two different kinds of road disturbances are inputted into the wheel. The dynamic responses of a quarter-vehicle model are simulated and compared with the simulation software Adams/View for the validity of the theoretical method. Full article
(This article belongs to the Special Issue Advances in Control for Electric Vehicle)
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25 pages, 8451 KiB  
Article
Integrated Yaw Stability Control of Electric Vehicle Equipped with Front/Rear Steer-by-Wire Systems and Four In-Wheel Motors
by Younghoon Seo, Kwanghyun Cho and Kanghyun Nam
Electronics 2022, 11(8), 1277; https://doi.org/10.3390/electronics11081277 - 18 Apr 2022
Cited by 5 | Viewed by 3385
Abstract
This paper presents the integrated motion control method for an electric vehicle (EV) equipped with a front/rear steer-by-wire (SbW) system and four in-wheel motor (IWM). The proposed integrated motion control method aims to maintain stable cornering. To maintain vehicle agility and stability, the [...] Read more.
This paper presents the integrated motion control method for an electric vehicle (EV) equipped with a front/rear steer-by-wire (SbW) system and four in-wheel motor (IWM). The proposed integrated motion control method aims to maintain stable cornering. To maintain vehicle agility and stability, the lateral force and yaw rate commands of the vehicle are generated by referring to the neutral steering characteristics. The driver’s driving force command, the lateral force command based on the bicycle model, and the yaw moment generated by the high-level controller are distributed into the driving force of each wheel and the lateral force of the front and rear wheels by the yaw moment distribution. Finally, the distributed forces are directly controlled by a low-level controller. To directly control the forces, a driving force observer and a lateral force observer were introduced via driving force estimation in the IWMs and rack force estimation in the SbW system. The control performance is verified through computer simulations. Full article
(This article belongs to the Special Issue Advances in Control for Electric Vehicle)
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19 pages, 4195 KiB  
Article
Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving
by Yonghwan Jeong and Seongjin Yim
Electronics 2021, 10(22), 2812; https://doi.org/10.3390/electronics10222812 - 16 Nov 2021
Cited by 31 | Viewed by 7198
Abstract
This paper presents an MPC-based integrated control algorithm for an autonomous vehicle equipped with four-wheel independent steering and driving systems. The objective of this research is to improve the performance of the path and velocity tracking controllers by distributing the control effort to [...] Read more.
This paper presents an MPC-based integrated control algorithm for an autonomous vehicle equipped with four-wheel independent steering and driving systems. The objective of this research is to improve the performance of the path and velocity tracking controllers by distributing the control effort to the multiple actuators. The proposed algorithm has two modules: reference state decision and MPC-based vehicle motion controller. Reference state decision module determines reference state profiles consisting of yaw rate and velocity in order to overcome the limitation of the error dynamics-based path tracking controller, which requires several assumptions on the reference path. The MPC-based vehicle motion controller is designed with a linear time-varying vehicle model in order to optimally allocate the control effort to each actuator. A linear time-varying MPC is adopted to reduce computational burden caused by using a non-linear one. The effectiveness of the proposed algorithm is validated via simulation on MATLAB/Simulink and CarSim. The simulation results show that the proposed algorithm improves the reference tracking performance by effectively distributing the control effort to the steering angle and driving force of each actuator. Full article
(This article belongs to the Special Issue Advances in Control for Electric Vehicle)
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16 pages, 3284 KiB  
Article
Rollover Prevention Control for Autonomous Electric Road Sweeper
by Seongjin Yim and Wongun Kim
Electronics 2021, 10(22), 2790; https://doi.org/10.3390/electronics10222790 - 14 Nov 2021
Cited by 1 | Viewed by 2986
Abstract
This paper presents a method to prevent the rollover of autonomous electric road sweepers (AERS). AERS have an articulated frame steering (AFS) mechanism. Moreover, the heights of the center of gravity of the front and rear bodies are high. As such, they are [...] Read more.
This paper presents a method to prevent the rollover of autonomous electric road sweepers (AERS). AERS have an articulated frame steering (AFS) mechanism. Moreover, the heights of the center of gravity of the front and rear bodies are high. As such, they are prone to rolling over at low speeds and at small articulation angles. A bicycle model with a nonlinear tire model was used as a vehicle model for AERS. Using that vehicle model, path tracking and speed controllers were designed in order to follow a predefined path and speed profile, respectively. To check the rollover propensity of AERS, load transfer ratio (LTR) based the rollover analysis was completed. Based on the results of the analysis, a rollover prevention scheme was proposed. To validate the proposed scheme, a simulation was conducted using a U-shaped path under constant speed conditions. From the simulation, it was shown that the proposed scheme is effective in preventing AERS from rolling over. Full article
(This article belongs to the Special Issue Advances in Control for Electric Vehicle)
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14 pages, 27508 KiB  
Article
Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics
by Chul-Jun Lee, Bo-Kyong Kim, Mi-Kyeong Kwon, Kanghyun Nam and Seok-Won Kang
Electronics 2021, 10(7), 846; https://doi.org/10.3390/electronics10070846 - 1 Apr 2021
Cited by 17 | Viewed by 4367
Abstract
We propose a robust and reliable method based on deep neural networks to estimate the remaining useful life of lithium-ion batteries in electric vehicles. In general, the degradation of a battery can be predicted by monitoring its internal resistance. However, prediction under battery [...] Read more.
We propose a robust and reliable method based on deep neural networks to estimate the remaining useful life of lithium-ion batteries in electric vehicles. In general, the degradation of a battery can be predicted by monitoring its internal resistance. However, prediction under battery operation cannot be achieved using conventional methods such as electrochemical impedance spectroscopy. The battery state can be predicted based on the change in the capacity according to the state of health. For the proposed method, a statistical analysis of capacity fade considering the impedance increase according to the degree of deterioration is conducted by applying a deep neural network to diverse data from charge/discharge characteristics. Then, probabilistic predictions based on the capacity fade trends are obtained to improve the prediction accuracy of the remaining useful life using another deep neural network. Full article
(This article belongs to the Special Issue Advances in Control for Electric Vehicle)
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