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Advanced Modeling, Analysis and Control for Electrified Vehicles

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A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 25398

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Special Issue Editors

Dr. Xianjian Jin

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Guest Editor

Department of Mechanical Engineering, Shanghai University, Shanghai 200072, China
Interests: vehicle system dynamics and control; advanced electric vehicles; automated driving; intelligent and connected vehicles

Dr. Chongfeng Wei

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Guest Editor

School of Mechanical and Aerospace Engineering, Queen‘s University Belfast, Belfast BR7 1NN, UK
Interests: intelligent vehicles; decision making and control

Dr. Chao Huang

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Guest Editor

Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: human–machine collaborative control; decision making; path planning; fault-tolerant control with the application of automated vehicles
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Dr. Chuan Hu

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Guest Editor

Department of Mechanical Engineering, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
Interests: decision-making; motion planning and control of connected and autonomous vehicles; unmanned vehicles and mobile robots
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Guodong Yin

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Guest Editor

School of Mechanical Engineering, Southeast University, Nanjing 211189, China
Interests: vehicle dynamic and control; electric vehicles; automatic and connected vehicles; intelligent manufacture
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Prof. Dr. Mohammed Chadli

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Guest Editor

Univ Evry Department UFR Sciences and Technologies, Université Paris-Saclay, 91020 Evry, France
Interests: fuzzy/polynomial/LPV/singular systems; LMI/SOS; FDI; FTC; automotive control; intelligent vehicle; renewable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleague,

Recently, electrified vehicles, especially fully electric ground vehicles, are expected to significantly provide increased traffic mobility and road utilization with faster response times, less fuel consumption and environmental pollution with electrified power sources and actuators, greater driving safety benefits and convenience integrated with diverse dynamics subsystems. As an important part of future smart transportation systems, electrified vehicles have attracted increasing concern from academia, industry and governments worldwide due to their advantages and applications.

However, emerging electrified vehicles could bring many challenges, such as the development of open electrified vehicle architectures and standards across different platforms; advanced dynamics modeling and control technologies in the presence of system nonlinearities, actuator saturation, and parameters uncertainties; accurate state-estimation schemes using low-cost sensors and extended information fusion in hub units; hybrid powertrain configurations and energy optimization strategies for maximizing the true potential; vibration suppression of in-wheel motor-active suspensions against negative electromechanical coupling influences; and early component fault detection and isolation to avoid dangerous drivability.

Dr. Xianjian Jin
Dr. Chongfeng Wei
Dr. Chao Huang
Dr. Chuan Hu
Prof. Dr. Guodong Yin
Prof. Dr. Mohammed Chadli
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. Machines 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 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

electrified vehicles
dynamics modeling
architecture analysis
fault diagnosis，control technologies

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Published Papers (11 papers)

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Editorial

Jump to: Research

4 pages, 193 KiB

Open AccessEditorial

Advanced Modeling, Analysis and Control for Electrified Vehicles

by Xianjian Jin, Chongfeng Wei, Chao Huang, Chuan Hu, Guodong Yin and Mohammed Chadli

Machines 2023, 11(9), 866; https://doi.org/10.3390/machines11090866 - 29 Aug 2023

Cited by 1 | Viewed by 981

Abstract

Electrified vehicles, especially fully driven electric ground vehicles, are expected to provide significantly increased traffic mobility and road utilization with faster response times, lower levels of fuel consumption, less environmental pollution, electrified power sources and actuators, and the benefits of greater driving safety [...] Read more.

(This article belongs to the Special Issue Advanced Modeling, Analysis and Control for Electrified Vehicles)

Research

Jump to: Editorial

21 pages, 5698 KiB

Open AccessArticle

Optimal Coordinated Control of Active Front Steering and Direct Yaw Moment for Distributed Drive Electric Bus

by Jiming Lin, Teng Zou, Liang Su, Feng Zhang and Yong Zhang

Machines 2023, 11(6), 640; https://doi.org/10.3390/machines11060640 - 11 Jun 2023

Cited by 3 | Viewed by 1395

Abstract

This paper suggests a hierarchical coordination control strategy to enhance the stability of distributed drive electric bus. First, an observer based on sliding mode observer (SMO) and adaptive neural fuzzy inference system (ANFIS) was designed to estimate the vehicle state parameters. Then the upper layer of the strategy primarily focuses on coordinating active front steering (AFS) and direct yaw moment control (DYC). The phase plane method is utilized in this layer to provide an assessment basis for the switching control safety of AFS and DYC. The lower layer of the strategy designs an integral terminal sliding mode controller (ITSMC) and a non-singular fast terminal sliding mode controller (NFTSMC) to obtain the optimal additional front wheel steering angle to improve handling performance. A fuzzy sliding mode controller (FSMC) is also proposed to obtain additional yaw moment to ameliorate yaw stability. Finally, the strategy proposed in this paper is subjected to simulation testing and compared with the performance of AFS and DYC systems. The proposed strategy is also evaluated for tracking errors in sideslip angle and yaw rate under two conditions. The results demonstrate that the proposed strategy can effectively adapt to various extreme environments and improve the maneuvering and yaw stability of the bus. Full article

(This article belongs to the Special Issue Advanced Modeling, Analysis and Control for Electrified Vehicles)

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15 pages, 6949 KiB

Open AccessCommunication

Research on Braking Energy Regeneration for Hybrid Electric Vehicles

by Mengtian Xu, Jianxin Peng, Xiaochen Ren, Xuekun Yang and Yuhui Hu

Machines 2023, 11(3), 347; https://doi.org/10.3390/machines11030347 - 3 Mar 2023

Cited by 3 | Viewed by 2861

Abstract

In recent years, there has been a significant increase in braking energy regeneration for hybrid electric vehicles. To improve performance and reduce fuel consumption, a better control strategy composed of braking regeneration and gear shifting is needed. This work presents a braking energy regeneration control strategy for a hybrid electric vehicle (HEV). The mathematical model for the vehicle dynamic system is established, and the objective function of braking energy regeneration is presented based on system analysis. Taking the increased electric energy of a battery as the objective function of the economic downshift law, the multi-island genetic algorithm (MIGA) is used to solve the shifting condition factors corresponding to different deceleration speeds and motor torques and the optimal downshifting speed. The presented control strategy of braking energy regeneration is validated in a typical city cycle form in China, and the results show better energy efficiency. Full article

(This article belongs to the Special Issue Advanced Modeling, Analysis and Control for Electrified Vehicles)

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19 pages, 11666 KiB

Open AccessArticle

An Integrated Obstacle Avoidance Controller Based on Scene-Adaptive Safety Envelopes

by Kang Li, Zhishuai Yin, Yuanxin Ba, Yue Yang, Yuanhao Kuang and Erqian Sun

Machines 2023, 11(2), 303; https://doi.org/10.3390/machines11020303 - 17 Feb 2023

Cited by 1 | Viewed by 1830

Abstract

This paper presents an integrated active obstacle avoidance controller in the Model Predictive Control (MPC) framework to ensure adaptive collision-free obstacle avoidance under complex scenarios while maintaining a good level of vehicle stability and steering smoothness. Firstly, with the observed road conditions and obstacle states as inputs, a data-driven Gaussian Process Regression (GPR) model is constructed and trained to generate confidence intervals, as scene-adaptive dynamic safety envelopes represent the safety boundaries of obstacle avoidance. Subsequently, the generated safety envelopes are transformed into soft and hard constraints, incorporated into the MPC controller and rolling updated in the prediction horizon to further cope with uncertain and rapidly evolving driving conditions. Minimizing both the control increments and stability feature parameters are formulated into the objectives of the MPC controller. By solving the multi-objective optimization problem with soft and hard constraints imposed, control commands are obtained to steer the vehicle in order to avoid the obstacles safely and smoothly with guaranteed vehicle stability. The experiments conducted on a motion-base driving simulator show that the proposed controller manages to perform safe and stable obstacle avoidance even under hazardous conditions. It is also verified that the proposed controller can be applied to more complex scenarios with dynamic obstacles presented. Full article

(This article belongs to the Special Issue Advanced Modeling, Analysis and Control for Electrified Vehicles)

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22 pages, 5061 KiB

Open AccessEditor’s ChoiceArticle

Integrated Adhesion Coefficient Estimation of 3D Road Surfaces Based on Dimensionless Data-Driven Tire Model

by Zhiwei Xu, Yongjie Lu, Na Chen and Yinfeng Han

Machines 2023, 11(2), 189; https://doi.org/10.3390/machines11020189 - 31 Jan 2023

Cited by 4 | Viewed by 1947

Abstract

The tire/road peak friction coefficient (TRPFC) is the core parameter of vehicle stability control, and its estimation accuracy significantly affects the control effect of active vehicle safety. To estimate the peak adhesion coefficient accurately, a new method for the comprehensive adhesion coefficient of three-dimensional pavement based on a dimensionless data-driven tire model is proposed. Firstly, in order to accurately describe the contact state between the three-dimensional road surface and the tire during driving, stress distribution and multi-point contact are introduced into the vertical dynamic model and a new tire model driven by dimensionless data is established based on the normalization method. Secondly, the real-time assessment of lateral and longitudinal adhesion coefficients of three-dimensional pavement is realized with the unscented Kalman filter (UKF). Finally, according to the coupling relationship between the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient, a fuzzy reasoning strategy of fusing the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient is designed. The results of vehicle tests prove that the method proposed in this paper can estimate the peak adhesion coefficient of pavement quickly and accurately. Full article

(This article belongs to the Special Issue Advanced Modeling, Analysis and Control for Electrified Vehicles)

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18 pages, 4462 KiB

Open AccessArticle

Anti-Rollover Control and HIL Verification for an Independently Driven Heavy Vehicle Based on Improved LTR

by Lufeng Zheng, Yongjie Lu, Haoyu Li and Junning Zhang

Machines 2023, 11(1), 117; https://doi.org/10.3390/machines11010117 - 14 Jan 2023

Cited by 5 | Viewed by 2328

Abstract

The rollover evaluation index provides an important threshold basis for the anti-rollover control system of vehicle. Regarding the rollover risk of independently driven heavy-duty vehicles, a new rollover evaluation index is proposed, and the feasibility of the improved index was verified through hierarchical control and HIL (hardware-in-the-loop) experiments. Based on an 18-DOF spatial dynamics model of a heavy-duty vehicle, the improved LTR (load transfer rate) index was obtained to describe the dynamic change in the tire’s vertical load. It replaces the suspension force and the vertical inertia force of the unsprung load mass. It avoids the problem of directly measuring or estimating the vertical load in the LTR index. Under the conditions of fishhooking and angle stepping, three types of rollover indicators were compared, and the proposed index can more sensitively identify the likelihood of rollover. In order to apply the improved rollover index to a rollover control well, a hierarchical controller based on the identification of the slip rate of the road surface, ABS control with sliding mode, variable structure and differential braking was designed. Simulations and HIL tests proved that the designed controller can accurately predict the rollover risk and avoid the rollover in time. Under the condition of J-turning, the yaw rate, slip angle and maximum lateral acceleration are reduced by 9%, 16% and 3%, respectively; under the condition of fishhooking, the maximum yaw rate, slip angle and lateral acceleration are reduced by 12%, 18% and 3%, respectively. Full article

(This article belongs to the Special Issue Advanced Modeling, Analysis and Control for Electrified Vehicles)

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21 pages, 2813 KiB

Open AccessArticle

Optimization of Gain Scheduled Controller for an Active Trailer Steering System Using an Evolutionary Algorithm

by Khizar Qureshi, Ramiro Liscano and Yuping He

Machines 2022, 10(11), 1019; https://doi.org/10.3390/machines10111019 - 3 Nov 2022

Cited by 1 | Viewed by 1564

Abstract

Car–trailer combinations can experience unstable motion modes such as trailer-sway, jackknifing and rollover that can lead to fatal accidents. These unstable motions can be mitigated with the use of an active trailer steering (ATS) system. Prior studies in ATS have leveraged the linear quadratic regulator (LQR) as an ATS controller but for many of these designs it was assumed that the vehicle and operating parameters were constant. In reality, vehicle and operating parameters may vary and have an impact on the stability of a car–trailer combination. In this paper, the weighting matrices of the LQR controller are determined using the GDE3 evolutionary optimization algorithm with the objective of addressing the design trade off between minimizing the car–trailer’s path-following performance for low vehicle speeds and minimizing the rearward amplification for high vehicle speeds. The effectiveness of the approach is demonstrated using a numerical simulation car–trailer model developed in the CarSim simulator. Our results show that the multi-objective tuned gain scheduling controller outperforms a non-tuned gain scheduling controller in terms of improving the lateral stability and the path following performance of car–trailer combinations in driver in the loop single lane-change maneuvers at a constant vehicle forward speed. Full article

(This article belongs to the Special Issue Advanced Modeling, Analysis and Control for Electrified Vehicles)

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23 pages, 8663 KiB

Open AccessArticle

Research on Tire–Road Parameters Estimation Algorithm for Skid-Steered Wheeled Unmanned Ground Vehicle

by Yuzheng Zhu, Xueyuan Li, Xing Zhang, Songhao Li, Qi Liu and Shihua Yuan

Machines 2022, 10(11), 1015; https://doi.org/10.3390/machines10111015 - 2 Nov 2022

Cited by 3 | Viewed by 1915

Abstract

Skid-steered wheeled vehicles can be applied in military, agricultural, and other fields because of their flexible layout structure and strong passability. The research and application of vehicles are developing towards the direction of “intelligent” and “unmanned”. As essential parts of unmanned vehicles, the motion planning and control systems are increasingly demanding for model and road parameters. In this paper, an estimation method for tire and road parameters is proposed by combining offline and online identification. Firstly, a 3-DOF nonlinear dynamic model is established, and the interaction between tire and road is described by the Brush nonlinear tire model. Then, the horizontal and longitudinal stiffness of the tire is identified offline using the particle swarm optimization (PSO) algorithm with adaptive inertia weight. Referring to the Burckhardt adhesion coefficient formula, the extended forgetting factor recursive least-squares (EFRLS) method is applied to identify the road adhesion coefficient online. Finally, the validity of the proposed identification algorithm is verified by TruckSim simulation and real vehicle tests. Results show that the relative error of the proposed algorithm can be well controlled within 5%. Full article

(This article belongs to the Special Issue Advanced Modeling, Analysis and Control for Electrified Vehicles)

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22 pages, 8134 KiB

Open AccessArticle

Study on the Control Algorithm of Automatic Emergency Braking System (AEBS) for Commercial Vehicle Based on Identification of Driving Condition

by Jianhua Guo, Yinhang Wang, Xingji Yin, Peng Liu, Zhuoran Hou and Di Zhao

Machines 2022, 10(10), 895; https://doi.org/10.3390/machines10100895 - 4 Oct 2022

Cited by 8 | Viewed by 3895

Abstract

Automatic emergency braking systems (AEBS) significantly improve the active safety performance of commercial vehicles, but their effectiveness is affected by the vehicle’s driving conditions, which mainly include the vehicle load and road conditions. In order to improve the adaptability of the AEBS, an AEBS control strategy with adaptive driving conditions was proposed and validated using a simulation and experimentation. This AEBS control strategy was designed based on an estimation of the vehicle mass, the center of gravity position, road grade, and the tire-road friction coefficient. In the simulation and experimental verification, the braking deceleration and braking distance under different driving conditions were compared. The results show that the AEBS control strategy proposed in this paper can avoid collisions in all test scenarios and maintain a parking spacing of approximately 5 m. In an extreme test scenario with a full load and low tire–road friction, as compared with the fixed threshold control strategy, the warning can be issued 0.2 s earlier and the maximum intensity braking can be carried out 0.5 s earlier. Full article

(This article belongs to the Special Issue Advanced Modeling, Analysis and Control for Electrified Vehicles)

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19 pages, 8435 KiB

Open AccessArticle

Integrated Control for Path Tracking and Stability Based on the Model Predictive Control for Four-Wheel Independently Driven Electric Vehicles

by Yunfeng Xie, Cong Li, Hui Jing, Weibiao An and Junji Qin

Machines 2022, 10(10), 859; https://doi.org/10.3390/machines10100859 - 26 Sep 2022

Cited by 4 | Viewed by 1824

Abstract

Four-wheel independently driven electric vehicles are prone to rollover when driving at high speeds on high-adhesion roads and to sideslip on low-adhesion roads, increasing the risks associated with such vehicles. To solve this problem, this study proposes a path tracking and stability-integrated controller based on a model predictive control algorithm. First, a vehicle planar dynamics model and a roll dynamics model are established, and the lateral velocity, yaw rate, roll angle, and roll angle velocity of the vehicle are estimated based on an unscented Kalman filter. The lateral stiffness of the tires is estimated online according to the real-time feedback state of the vehicle. Then, the path tracking controller, roll stability controller, and lateral stability controller are designed. An integrated control strategy is designed for the path tracking and stability, and the conditions and coordination strategies for the vehicle roll and lateral stability state in the path tracking are studied. The simulation results show that the proposed algorithm can effectively limit the lateral load transfer rate on high-adhesion roads and the sideslip angle on low-adhesion roads at high speeds. Hence, the driving stability of the vehicle under different road adhesion coefficients can be ensured and the path tracking performance can be improved. Full article

(This article belongs to the Special Issue Advanced Modeling, Analysis and Control for Electrified Vehicles)

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17 pages, 5425 KiB

Open AccessArticle

A Robust Gaussian Process-Based LiDAR Ground Segmentation Algorithm for Autonomous Driving

by Xianjian Jin, Hang Yang, Xin Liao, Zeyuan Yan, Qikang Wang, Zhiwei Li and Zhaoran Wang

Machines 2022, 10(7), 507; https://doi.org/10.3390/machines10070507 - 23 Jun 2022

Cited by 5 | Viewed by 2182

Abstract

Robust and precise vehicle detection is the prerequisite for decision-making and motion planning in autonomous driving. Vehicle detection algorithms follow three steps: ground segmentation, obstacle clustering and bounding box fitting. The ground segmentation result directly affects the input of the subsequent obstacle clustering algorithms. Aiming at the problems of over-segmentation and under-segmentation in traditional ground segmentation algorithms, a ground segmentation algorithm based on Gaussian process is proposed in this paper. To ensure accurate search of real ground candidate points as training data for Gaussian process, the proposed algorithm introduces the height and slope criteria, which is more reasonable than the use of fixed height threshold for searching. After that, a sparse covariance function is introduced as the kernel function for calculation in Gaussian process. This function is more suitable for ground segmentation situation the radial basis function (RBF). The proposed algorithm is tested on our autonomous driving experimental platform and the public autonomous driving dataset KITTI, compared with the most used RANSAC algorithm and ray ground filter algorithm. Experiment results show that the proposed algorithm can avoid obvious over-segmentation and under-segmentation. In addition, compared with the RBF, the introduction of the sparse covariance function also reduces the computation time by 37.26%. Full article

(This article belongs to the Special Issue Advanced Modeling, Analysis and Control for Electrified Vehicles)

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