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Artificial Intelligence Applied in Smart Electric Vehicles: Towards Eco-Driving for...
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Artificial Intelligence Applied in Smart Electric Vehicles: Towards Eco-Driving for Improved Energy Economy

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 March 2022) | Viewed by 12666

Special Issue Editors

Dr. Yuanjian Zhang

E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering, Queen's University Belfast, Belfast BT7 1NN, UK
Interests: vehicle dynamic and control; electric vehicles; automatic and connected vehicles, intelligent manufacture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guodong Yin

E-Mail Website
Guest Editor
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
Interests: vehicle dynamic and control; electric vehicles; automatic and connected vehicles; intelligent manufacture
Special Issues, Collections and Topics in MDPI journals
Dr. Nan Xu

E-Mail Website
Guest Editor
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Interests: human-machine-environment data mining and analysis; global optimization; vehicle powertrain control and evaluation, energy management; hybrid vehicles

Special Issue Information

Dear Colleagues,

Research on control and energy management schemes for electric vehicles (EVs) with multi-configurations has been ongoing for over 30 years, slowly moving towards intelligent EV methods underpinned by evolving advanced control methods, communication and sensing techniques, and Internet of Vehicles (IoVs). Smart EVs require resilient schemes that balance the desired control effect with the computational expense. However, many sophisticated methods are currently not suitable for application in smart EVs. As one hot spot of the research on personification decision, artificial intelligence (AI) has been broadly and meticulously studied, as it presents a huge potential in EV control and energy management implementation. Benefiting from superior ability in regression and classification analysis, AI methods pave new pathways to the state-of-art multi-scale control and energy management in EVs, e.g., AI-based battery degradation prediction, AI-based motion plans for automatic driving, etc. Moreover, AI-based schemes also have significant potential to overcome many of the barriers facing energy-saving in EVs.  

To inspire novel AI-based applications in smart EVs towards eco-driving, this Special Issue will seek fantastic solutions among high-quality submissions. The topics of interest cover AI-based control or energy management methods aiming at eco-driving for all-range configurations in EVs, including pure EVs, hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and fuel cell vehicles (FCVs).  

The suggested topics include, but are not limited to:   

  • AI-based control strategies for pure EVs, HEVs, PHEVs, FCVs;
  • AI-based multi-scale energy management in EVs, e.g. energy management problems in energy storage systems (battery state estimation, battery degradation prediction), powertrains, and vehicle dynamics;
  • AI-based eco-driving assistant systems for pure EVs, HEVs, PHEVs, FCVs;
  • AI-based control strategies in automatic driving with target to improve energy economy;
  • AI-based vehicle-environment co-operation schemes for eco-driving in pure EVs, HEVs, PHEVs, FCVs;
  • AI-based human-vehicle co-operation schemes for eco-driving in pure EVs, HEVs, PHEVs, FCVs;
  • AI-based EV fleet control methods for eco-driving.

Dr. Yuanjian Zhang
Prof. Dr. Guodong Yin
Dr. Nan Xu
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

  • Artificial Intelligence
  • electric vehicles
  • control methods
  • energy saving
  • eco-driving
  • vehicle–environment cooperation
  • human–vehicle cooperation

Published Papers (4 papers)

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Research

22 pages, 8903 KiB  
Article
Reinforcement-Learning-Based Decision and Control for Autonomous Vehicle at Two-Way Single-Lane Unsignalized Intersection
by Yonggang Liu, Gang Liu, Yitao Wu, Wen He, Yuanjian Zhang and Zheng Chen
Electronics 2022, 11(8), 1203; https://doi.org/10.3390/electronics11081203 - 10 Apr 2022
Cited by 7 | Viewed by 2269
Abstract
Intersections have attracted wide attention owing to their complexity and high rate of traffic accidents. In the process of developing L3-and-above autonomous-driving techniques, it is necessary to solve problems in autonomous driving decisions and control at intersections. In this article, a decision-and-control method [...] Read more.
Intersections have attracted wide attention owing to their complexity and high rate of traffic accidents. In the process of developing L3-and-above autonomous-driving techniques, it is necessary to solve problems in autonomous driving decisions and control at intersections. In this article, a decision-and-control method based on reinforcement learning and speed prediction is proposed to manage the conjunction of straight and turning vehicles at two-way single-lane unsignalized intersections. The key position of collision avoidance in the process of confluence is determined by establishing a road-geometry model, and on this basis, the expected speed of the straight vehicle that ensures passing safety is calculated. Then, a reinforcement-learning algorithm is employed to solve the decision-control problem of the straight vehicle, and the expected speed is optimized to direct the agent to learn and converge to the planned decision. Simulations were conducted to verify the performance of the proposed method, and the results show that the proposed method can generate proper decisions for the straight vehicle to pass the intersection while guaranteeing preferable safety and traffic efficiency. Full article
(This article belongs to the Special Issue Artificial Intelligence Applied in Smart Electric Vehicles: Towards Eco-Driving for Improved Energy Economy)
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20 pages, 8540 KiB  
Article
Prediction of AI-Based Personal Thermal Comfort in a Car Using Machine-Learning Algorithm
by Yeong Jo Ju, Jeong Ran Lim and Euy Sik Jeon
Electronics 2022, 11(3), 340; https://doi.org/10.3390/electronics11030340 - 23 Jan 2022
Cited by 3 | Viewed by 3524
Abstract
Defining a passenger’s thermal comfort in a car cabin is difficult because of the narrow environment and various parameters. Although passenger comfort is predicted using a thermal-comfort scale in the overall cabin or a local area, the scale’s range of passenger comfort may [...] Read more.
Defining a passenger’s thermal comfort in a car cabin is difficult because of the narrow environment and various parameters. Although passenger comfort is predicted using a thermal-comfort scale in the overall cabin or a local area, the scale’s range of passenger comfort may differ owing to psychological factors and individual preferences. Among the many factors affecting such comfort levels, the temperature of the seat is one of the direct and significant environmental factors. Therefore, it is necessary to predict the cabin environment and seat-related personal thermal comfort. Accordingly, machine learning is used in this research to predict whether a passenger’s seat-heating-operation pattern can be predicted in a winter environment. The experiment measures the ambient factor and collects data on passenger heating-operation patterns using a device in an actual winter environment. The temperature is set as the input parameter in the measured data and the operation pattern is used as the output parameter. Based on the parameters, the predictive accuracy of the heating-operation pattern is investigated using machine learning. The algorithms used in the machine-learning train are Tree, SVM, and kNN. In addition, the predictive accuracy is tested using SVM and kNN, which shows a high validation accuracy based on the prediction results of the algorithm. In this research, the parameters predicting the personal thermal comfort of three passengers are investigated as a combination of input parameters, according to the passengers. As a result, the predictive accuracy of the operation pattern according to the tested input parameter is 0.96, showing the highest accuracy. Considering each passenger, the predictive accuracy has a maximum deviation of 30%. However, we verify that it indicates the level of accuracy in predicting a passenger’s heating-operation pattern. Accordingly, the possibility of operating a heating seat without a switch operation is confirmed through machine learning. The primary-stage research result reveals whether it is possible to predict objective personal thermal comfort using the passenger seat’s heating-operation pattern. Based on the results of this research, it is expected to be utilized for system construction based on the AI prediction of operation patterns according to the passenger through machine learning. Full article
(This article belongs to the Special Issue Artificial Intelligence Applied in Smart Electric Vehicles: Towards Eco-Driving for Improved Energy Economy)
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17 pages, 4576 KiB  
Article
Optimization of Energy Consumption Based on Traffic Light Constraints and Dynamic Programming
by Jiaming Xing, Liang Chu and Chong Guo
Electronics 2021, 10(18), 2295; https://doi.org/10.3390/electronics10182295 - 17 Sep 2021
Cited by 2 | Viewed by 2406
Abstract
Traffic lights are an important part of urban roads. They improve traffic conditions but bring about a limitation of driving speed in the space–time domain for vehicles. In this paper, a traffic light model based on a vehicle–road cooperative system is built. The [...] Read more.
Traffic lights are an important part of urban roads. They improve traffic conditions but bring about a limitation of driving speed in the space–time domain for vehicles. In this paper, a traffic light model based on a vehicle–road cooperative system is built. The model provides the vehicle with speed constraints when passing the green light in the time–space domain. A global-optimization-based energy management strategy based on dynamic programming (DP) is constructed with the constraints. The simulations are performed for two driving situations of different signal phases with the electric vehicle driven by a single power source. Compared with the traditional fixed speed driving strategy and green light optimal speed advisory (GLOSA) system, the energy management strategy proposed in this paper is able to control operating points of the motor to be distributed in more efficiency areas. A higher economy is achieved from simulation results. Full article
(This article belongs to the Special Issue Artificial Intelligence Applied in Smart Electric Vehicles: Towards Eco-Driving for Improved Energy Economy)
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17 pages, 4695 KiB  
Article
A Novel Comprehensive Scheme for Vehicle State Estimation Using Dual Extended H-Infinity Kalman Filter
by Fengjiao Zhang, Yan Wang, Jingyu Hu, Guodong Yin, Song Chen, Hongdang Zhang and Dong Zhou
Electronics 2021, 10(13), 1526; https://doi.org/10.3390/electronics10131526 - 24 Jun 2021
Cited by 9 | Viewed by 1918
Abstract
The performance of vehicle active safety systems relies on accurate vehicle state information. Estimation of vehicle state based on onboard sensors has been popular in research due to technical and cost constraints. Although many experts and scholars have made a lot of research [...] Read more.
The performance of vehicle active safety systems relies on accurate vehicle state information. Estimation of vehicle state based on onboard sensors has been popular in research due to technical and cost constraints. Although many experts and scholars have made a lot of research efforts for vehicle state estimation, studies that simultaneously consider the effects of noise uncertainty and model parameter perturbation have rarely been reported. In this paper, a comprehensive scheme using dual Extended H-infinity Kalman Filter (EH∞KF) is proposed to estimate vehicle speed, yaw rate, and sideslip angle. A three-degree-of-freedom vehicle dynamics model is first established. Based on the model, the first EH∞KF estimator is used to identify the mass of the vehicle. Simultaneously, the second EH∞KF estimator uses the result of the first estimator to predict the vehicle speed, yaw rate, and sideslip angle. Finally, simulation tests are carried out to demonstrate the effectiveness of the proposed method. The test results indicate that the proposed method has higher estimation accuracy than the extended Kalman filter. Full article
(This article belongs to the Special Issue Artificial Intelligence Applied in Smart Electric Vehicles: Towards Eco-Driving for Improved Energy Economy)
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