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Research Progress on Intelligent Electric Vehicles

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 19601

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Special Issue Editors

Dr. Jinghua Guo

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Guest Editor

Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 384002, China
Interests: multi-agent system; distributed control; intelligent driving; advanced sensors
Special Issues, Collections and Topics in MDPI journals

Dr. Jingyao Wang

E-Mail Website
Guest Editor

Department of Automation, Xiamen University, Xiamen 384002,China
Interests: intelligent electric vehicles; vehicle dynamics and control; vision system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent electric vehicles are equipped with advanced sensors and electronic systems, such as vision system, global positioning system, wireless communication network, and so on, and have received much research interest as an effective method to reduce the energy consumption and enhance the traffic safety and efficiency of intelligent transportation systems. The development and application of intelligent electric vehicles require a variety of technologies, including environmental perception, intelligent decision, information safety, human–machine shared driving, vehicle dynamics control, etc. The above technologies have encountered new challenges, due to a new round of scientific and technological revolution represented by mobile Internet, big data and cloud computing.

This Special Issue addresses new environmental perception, intelligent decision, information safety, and vehicle dynamics control methods of intelligent electric vehicles. Survey papers also welcome.

Prof. Dr. Yugong Luo
Dr. Jinghua Guo
Dr. Jingyao 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.

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Keywords

intelligent electric vehicles
advanced sensors
environmental perception
intelligent decision
information safety
vehicle dynamics and control

Published Papers (7 papers)

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Research

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14 pages, 2753 KiB

Open AccessArticle

Longitudinal Predictive Control for Vehicle-Following Collision Avoidance in Autonomous Driving Considering Distance and Acceleration Compensation

by Shutong Yin, Chunlin Yang, Ibna Kawsar, Haifeng Du and Yongjun Pan

Sensors 2022, 22(19), 7395; https://doi.org/10.3390/s22197395 - 28 Sep 2022

Cited by 4 | Viewed by 1726

Abstract

In response to the widespread adoption of vehicle-following systems in autonomous applications, the demand for collision warning to enable safer functionalities is increasing. This study provides an approach for automated vehicle guidance to follow the preceding vehicles longitudinally and puts emphasis on the performance of collision avoidance. The safety distance model is established, which contains a distance compensation algorithm to deal with the special case on curved roads. By introducing the algorithm of velocity and distance prediction, the collision risks are detected and measured in real time. The objective function is established based on optimal control theory to solve the desired following acceleration. The control system designed with the method of proportion integration differentiation combines throttle percentage and brake pressure as outputs to compensate acceleration. In the Carsim and Simulink co-simulation platform, the control system for longitudinal collision avoidance is simulated and analysed for four typical working conditions: the preceding vehicle drives at a constant speed on straight and curved roads, while the preceding vehicle drives at various speeds on straight and curved roads. The results validate the feasibility and effectiveness of the proposed method, which can be used for the longitudinal control of vehicle-following active collision avoidance. Full article

(This article belongs to the Special Issue Research Progress on Intelligent Electric Vehicles)

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25 pages, 11818 KiB

Open AccessArticle

Multi Source Electric Vehicles: Smooth Transition Algorithm for Transient Ripple Minimization

by Adel Oubelaid, Nabil Taib, Toufik Rekioua, Mohit Bajaj, Vojtech Blazek, Lukas Prokop, Stanislav Misak and Sherif S. M. Ghoneim

Sensors 2022, 22(18), 6772; https://doi.org/10.3390/s22186772 - 7 Sep 2022

Cited by 20 | Viewed by 1734

Abstract

Any engineering system involves transitions that reduce the performance of the system and lower its comfort. In the field of automotive engineering, the combination of multiple motors and multiple power sources is a trend that is being used to enhance hybrid electric vehicle (HEV) propulsion and autonomy. However, HEV riding comfort is significantly reduced because of high peaks that occur during the transition from a single power source to a multisource powering mode or from a single motor to a multiple motor traction mode. In this study, a novel model-based soft transition algorithm (STA) is used for the suppression of large transient ripples that occur during HEV drivetrain commutations and power source switches. In contrast to classical abrupt switching, the STA detects transitions, measures their rates, generates corresponding transition periods, and uses adequate transition functions to join the actual and the targeted operating points of a given HEV system variable. As a case study, the STA was applied to minimize the transition ripples that occur in a fuel cell-supercapacitor HEV. The transitions that occurred within the HEV were handled using two proposed transition functions which were: a linear-based transition function and a stair-based transition function. The simulation results show that, in addition to its ability to improve driving comfort by minimizing transient torque ripples and DC bus voltage fluctuations, the STA helps to increase the lifetime of the motor and power sources by reducing the currents drawn during the transitions. It is worth noting that the considered HEV runs on four-wheel drive when the load torque applied on it exceeds a specified torque threshold; otherwise, it operates in rear-wheel drive. Full article

(This article belongs to the Special Issue Research Progress on Intelligent Electric Vehicles)

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20 pages, 8410 KiB

Open AccessArticle

Visual Detection and Image Processing of Parking Space Based on Deep Learning

by Chen Huang, Shiyue Yang, Yugong Luo, Yongsheng Wang and Ze Liu

Sensors 2022, 22(17), 6672; https://doi.org/10.3390/s22176672 - 3 Sep 2022

Cited by 7 | Viewed by 4222

Abstract

The automatic parking system based on vision is greatly affected by uneven lighting, which is difficult to make an accurate judgment on parking spaces in the case of complex image information. To solve this problem, this paper proposes a parking space visual detection and image processing method based on deep learning. Firstly, a 360-degree panoramic system was designed to photograph the vehicle environment. The image has been processed to obtain a panoramic aerial view, which was input as the original image of the parking space detection system. Secondly, the Faster R-CNN (Region-Convolutional Neural Network) parking detection model was established based on deep learning. It was aimed to detect and extract the parking space from the input image. Thirdly, the problems of uneven illumination and complex background were solved effectively by removing the background light from the image. Finally, a parking space extraction method based on the connected region has been designed, which further simplified the parking space extraction and image processing. The experiment results show that the mAP (mean Average Precision) value of the Faster R-CNN model using 101-Floor ResNet as the feature extraction network is 89.30%, which is 2.28% higher than that of the Faster R-CNN model using 50-Floor ResNet as the feature extraction network. The model built in this paper can detect most parking spaces well. The position of the output target box is accurate. In some test scenarios, the confidence of parking space recognition can even reach 100%. In summary, the proposed method can realize the effective identification and accurate positioning of parking spaces. Full article

(This article belongs to the Special Issue Research Progress on Intelligent Electric Vehicles)

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31 pages, 6684 KiB

Open AccessArticle

Robust Optimization and Power Management of a Triple Junction Photovoltaic Electric Vehicle with Battery Storage

by Salah Beni Hamed, Mouna Ben Hamed, Lassaad Sbita, Mohit Bajaj, Vojtech Blazek, Lukas Prokop, Stanislav Misak and Sherif S. M. Ghoneim

Sensors 2022, 22(16), 6123; https://doi.org/10.3390/s22166123 - 16 Aug 2022

Cited by 17 | Viewed by 1719

Abstract

This paper highlights a robust optimization and power management algorithm that supervises the energy transfer flow to meet the photovoltaic (PV) electric vehicle demand, even when the traction system is in motion. The power stage of the studied system consists of a triple-junction PV generator as the main energy source, a lithium-ion battery as an auxiliary energy source, and an electric vehicle. The input–output signal adaptation is made by using a stage of energy conversion. A bidirectional DC-DC buck-boost connects the battery to the DC-link. Two unidirectional boost converters interface between the PV generator and the DC link. One is controlled with a maximum power point tracking (MPPT) algorithm to reach the maximum power points. The other is used to control the voltage across the DC-link. The converters are connected to the electric vehicle via a three-phase inverter via the same DC-link. By considering the nonlinear behavior of these elements, dynamic models are developed. A robust nonlinear MPPT algorithm has been developed owing to the nonlinear dynamics of the PV generator, metrological condition variations, and load changes. The high performance of the MPPT algorithm is effectively highlighted over a comparative study with two classical P & O and the fuzzy logic MPPT algorithms. A nonlinear control based on the Lyapunov function has been developed to simultaneously regulate the DC-link voltage and control battery charging and discharging operations. An energy management rule-based strategy is presented to effectively supervise the power flow. The conceived system, energy management, and control algorithms are implemented and verified in the Matlab/Simulink environment. Obtained results are presented and discussed under different operating conditions. Full article

(This article belongs to the Special Issue Research Progress on Intelligent Electric Vehicles)

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22 pages, 9580 KiB

Open AccessArticle

Model Predictive Direct Torque Control and Fuzzy Logic Energy Management for Multi Power Source Electric Vehicles

by Khoudir Kakouche, Toufik Rekioua, Smail Mezani, Adel Oubelaid, Djamila Rekioua, Vojtech Blazek, Lukas Prokop, Stanislav Misak, Mohit Bajaj and Sherif S. M. Ghoneim

Sensors 2022, 22(15), 5669; https://doi.org/10.3390/s22155669 - 28 Jul 2022

Cited by 43 | Viewed by 3389

Abstract

This paper proposes a novel Fuzzy-MPDTC control applied to a fuel cell battery electric vehicle whose traction is ensured using a permanent magnet synchronous motor (PMSM). On the traction side, model predictive direct torque control (MPDTC) is used to control PMSM torque, and guarantee minimum torque and current ripples while ensuring satisfactory speed tracking. On the sources side, an energy management strategy (EMS) based on fuzzy logic is proposed, it aims to distribute power over energy sources rationally and satisfy the load power demand. To assess these techniques, a driving cycle under different operating modes, namely cruising, acceleration, idling and regenerative braking is proposed. Real-time simulation is developed using the RT LAB platform and the obtained results match those obtained in numerical simulation using MATLAB/Simulink. The results show a good performance of the whole system, where the proposed MPDTC minimized the torque and flux ripples with 54.54% and 77%, respectively, compared to the conventional DTC and reduced the THD of the PMSM current with 53.37%. Furthermore, the proposed EMS based on fuzzy logic shows good performance and keeps the battery SOC within safe limits under the proposed speed profile and international NYCC driving cycle. These aforementioned results confirm the robustness and effectiveness of the proposed control techniques. Full article

(This article belongs to the Special Issue Research Progress on Intelligent Electric Vehicles)

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19 pages, 40258 KiB

Open AccessArticle

A Composite DC–DC Converter Based on the Versatile Buck–Boost Topology for Electric Vehicle Applications

by Catalina González-Castaño, Carlos Restrepo, Freddy Flores-Bahamonde and Jose Rodriguez

Sensors 2022, 22(14), 5409; https://doi.org/10.3390/s22145409 - 20 Jul 2022

Cited by 9 | Viewed by 2744

Abstract

The composite converter allows integrating the high-efficiency converter modules to achieve superior efficiency performance, becoming a prominent solution for electric transport power conversion. In this work, the versatile buck–boost dc–dc converter is proposed to be integrated into an electric vehicle composite architecture that requires a wide voltage range in the dc link to improve the electric motor efficiency. The inductor core of this versatile buck–boost converter has been redesigned for high voltage applications. The versatile buck–boost converter module of the composite architecture is in charge of the control stage. It provides a dc bus voltage regulation at a wide voltage operation range, which requires step-up (boost) and step-down (buck) operating modes. The PLECS thermal simulation of the composite architecture shows a superior power conversion efficiency of the proposed topology over the well-known classical noninverting buck–boost converter under the same operating conditions. The obtained results have been validated via experimental efficiency measures and experimental transient responses of the versatile buck–boost converter. Finally, a hardware-in-the-loop (HIL) real-time simulation system of a 4.4 kW powertrain is presented using a PLECS RT Box 1 device. The HIL simulation results verified the accuracy of the theoretical analysis and the effectiveness of the proposed architecture. Full article

(This article belongs to the Special Issue Research Progress on Intelligent Electric Vehicles)

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Review

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24 pages, 1456 KiB

Open AccessReview

Resilient Consensus Control for Multi-Agent Systems: A Comparative Survey

by Jingyao Wang, Xingming Deng, Jinghua Guo and Zeqin Zeng

Sensors 2023, 23(6), 2904; https://doi.org/10.3390/s23062904 - 7 Mar 2023

Cited by 5 | Viewed by 3087

Abstract

Due to the openness of communication network and the complexity of system structures, multi-agent systems are vulnerable to malicious network attacks, which can cause intense instability to these systems. This article provides a survey of state-of-the-art results of network attacks on multi-agent systems. Recent advances on three types of attacks, i.e., those on DoS attacks, spoofing attacks and Byzantine attacks, the three main network attacks, are reviewed. Their attack mechanisms are introduced, and the attack model and the resilient consensus control structure are discussed, respectively, in detail, in terms of the theoretical innovation, the critical limitations and the change of the application. Moreover, some of the existing results along this line are given in a tutorial-like fashion. In the end, some challenges and open issues are indicated to guide future development directions of the resilient consensus of multi-agent system under network attacks. Full article

(This article belongs to the Special Issue Research Progress on Intelligent Electric Vehicles)

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