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Integrated Control and Sensing Technology for Electric Vehicles

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 4066

Special Issue Editors


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Guest Editor
Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: autonomous driving, electric vehicles and intelligent systems; new generation clean propulsion control and optimisation, digital modelling and simulation; intelligent transportation system and artificial intelligence (AI) in engineering practice
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: shared control (i.e., human–machine interaction); development of advanced driver assistant system (adas); autonomous vehicles; traffic control
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of California, Merced, CA 95343, USA
Interests: vehicle dynamics; vehicle control; electric vehicles; hybrid-electric vehicles; autonomous driving
Special Issues, Collections and Topics in MDPI journals
Birmingham CASE Automotive Research and Education Centre, School of Engineering, University of Birmingham, Birmingham B15 2SQ, UK
Interests: energy management; hybrid and electric vehicles; driving behavior; man-machine system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electric vehicles (EVs) are an emerging technology that has a positive influence on ecology and the environment around the world through the automobile industry. It is expected that more vehicles will be electrified in the coming years. Vehicle control systems are control commands directed to vehicle actuators/sensors that control steering, throttle, and braking, as well as other related commands to support a safe transition between manual and automatic vehicle control. Additionally, the integrated control for electric vehicles, which performs the vehicle dynamic control and electric powertrain control cooperatively.

This Special Issue aims to provide a unique platform to publish state-of-the-art integrated control frameworks and strategies for electric vehicles. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • electric vehicles (EVs)
  • integrated vehicle control
  • sensor fusion
  • vehicle sensors/actuators
  • energy management optimization
  • vehicle dynamic control
  • electric powertrain control

Dr. Yuanjian Zhang
Dr. Jingjing Jiang
Dr. Ricardo De Castro
Dr. Ji Li
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. Sensors 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 2600 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.

Published Papers (5 papers)

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Research

20 pages, 4317 KiB  
Article
Vehicle Position Detection Based on Machine Learning Algorithms in Dynamic Wireless Charging
by Milad Behnamfar, Alexander Stevenson, Mohd Tariq and Arif Sarwat
Sensors 2024, 24(7), 2346; https://doi.org/10.3390/s24072346 - 07 Apr 2024
Viewed by 385
Abstract
Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented [...] Read more.
Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle’s position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position. Full article
(This article belongs to the Special Issue Integrated Control and Sensing Technology for Electric Vehicles)
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13 pages, 3218 KiB  
Article
Enhancing Urban Mobility with Self-Tuning Fuzzy Logic Controllers for Power-Assisted Bicycles in Smart Cities
by Jin-Shyan Lee, Ze-Hua Chen and Yue Hong
Sensors 2024, 24(5), 1552; https://doi.org/10.3390/s24051552 - 28 Feb 2024
Viewed by 511
Abstract
In smart cities, bicycle-sharing systems have become an essential component of the transportation services available in major urban centers around the globe. Due to environmental sustainability, research on the power-assisted control of electric bikes has attracted much attention. Recently, fuzzy logic controllers (FLCs) [...] Read more.
In smart cities, bicycle-sharing systems have become an essential component of the transportation services available in major urban centers around the globe. Due to environmental sustainability, research on the power-assisted control of electric bikes has attracted much attention. Recently, fuzzy logic controllers (FLCs) have been successfully applied to such systems. However, most existing FLC approaches have a fixed fuzzy rule base and cannot adapt to environmental changes, such as different riders and roads. In this paper, a modified FLC, named self-tuning FLC (STFLC), is proposed for power-assisted bicycles. In addition to a typical FLC, the presented scheme adds a rule-tuning module to dynamically adjust the rule base during fuzzy inference processes. Simulation and experimental results indicate that the presented self-tuning module leads to comfortable and safe riding as compared with other approaches. The technique established in this paper is thought to have the potential for broader application in public bicycle-sharing systems utilized by a diverse range of riders. Full article
(This article belongs to the Special Issue Integrated Control and Sensing Technology for Electric Vehicles)
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16 pages, 2302 KiB  
Article
Query-Informed Multi-Agent Motion Prediction
by Chong Guo, Shouyi Fan, Chaoyi Chen, Wenbo Zhao, Jiawei Wang, Yao Zhang and Yanhong Chen
Sensors 2024, 24(1), 9; https://doi.org/10.3390/s24010009 - 19 Dec 2023
Viewed by 676
Abstract
In a dynamic environment, autonomous driving vehicles require accurate decision-making and trajectory planning. To achieve this, autonomous vehicles need to understand their surrounding environment and predict the behavior and future trajectories of other traffic participants. In recent years, vectorization methods have dominated the [...] Read more.
In a dynamic environment, autonomous driving vehicles require accurate decision-making and trajectory planning. To achieve this, autonomous vehicles need to understand their surrounding environment and predict the behavior and future trajectories of other traffic participants. In recent years, vectorization methods have dominated the field of motion prediction due to their ability to capture complex interactions in traffic scenes. However, existing research using vectorization methods for scene encoding often overlooks important physical information about vehicles, such as speed and heading angle, relying solely on displacement to represent the physical attributes of agents. This approach is insufficient for accurate trajectory prediction models. Additionally, agents’ future trajectories can be diverse, such as proceeding straight or making left or right turns at intersections. Therefore, the output of trajectory prediction models should be multimodal to account for these variations. Existing research has used multiple regression heads to output future trajectories and confidence, but the results have been suboptimal. To address these issues, we propose QINET, a method for accurate multimodal trajectory prediction for all agents in a scene. In the scene encoding part, we enhance the feature attributes of agent vehicles to better represent the physical information of agents in the scene. Our scene representation also possesses rotational and spatial invariance. In the decoder part, we use cross-attention and induce the generation of multimodal future trajectories by employing a self-learned query matrix. Experimental results demonstrate that QINET achieves state-of-the-art performance on the Argoverse motion prediction benchmark and is capable of fast multimodal trajectory prediction for multiple agents. Full article
(This article belongs to the Special Issue Integrated Control and Sensing Technology for Electric Vehicles)
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18 pages, 3678 KiB  
Article
Intelligent Vehicle Decision-Making and Trajectory Planning Method Based on Deep Reinforcement Learning in the Frenet Space
by Jiawei Wang, Liang Chu, Yao Zhang, Yabin Mao and Chong Guo
Sensors 2023, 23(24), 9819; https://doi.org/10.3390/s23249819 - 14 Dec 2023
Cited by 1 | Viewed by 1003
Abstract
The complexity inherent in navigating intricate traffic environments poses substantial hurdles for intelligent driving technology. The continual progress in mapping and sensor technologies has equipped vehicles with the capability to intricately perceive their exact position and the intricate interplay among surrounding traffic elements. [...] Read more.
The complexity inherent in navigating intricate traffic environments poses substantial hurdles for intelligent driving technology. The continual progress in mapping and sensor technologies has equipped vehicles with the capability to intricately perceive their exact position and the intricate interplay among surrounding traffic elements. Building upon this foundation, this paper introduces a deep reinforcement learning method to solve the decision-making and trajectory planning problem of intelligent vehicles. The method employs a deep learning framework for feature extraction, utilizing a grid map generated from a blend of static environmental markers such as road centerlines and lane demarcations, in addition to dynamic environmental cues including vehicle positions across varied lanes, all harmonized within the Frenet coordinate system. The grid map serves as the input for the state space, and the input for the action space comprises a vector encompassing lane change timing, velocity, and vertical displacement at the lane change endpoint. To optimize the action strategy, a reinforcement learning approach is employed. The feasibility, stability, and efficiency of the proposed method are substantiated via experiments conducted in the CARLA simulator across diverse driving scenarios, and the proposed method can increase the average success rate of lane change by 6.8% and 13.1% compared with the traditional planning control algorithm and the simple reinforcement learning method. Full article
(This article belongs to the Special Issue Integrated Control and Sensing Technology for Electric Vehicles)
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20 pages, 7776 KiB  
Article
Parameter Optimization of Model Predictive Direct Motion Control for Distributed Drive Electric Vehicles Considering Efficiency and the Driving Feeling
by Lixiao Gao and Feng Chai
Sensors 2023, 23(14), 6324; https://doi.org/10.3390/s23146324 - 12 Jul 2023
Viewed by 815
Abstract
This paper presents a novel motion control strategy based on model predictive control (MPC) for distributed drive electric vehicles (DDEVs), aiming to simultaneously control the longitudinal and lateral motion while considering efficiency and the driving feeling. Initially, we analyze the vehicle’s dynamic model, [...] Read more.
This paper presents a novel motion control strategy based on model predictive control (MPC) for distributed drive electric vehicles (DDEVs), aiming to simultaneously control the longitudinal and lateral motion while considering efficiency and the driving feeling. Initially, we analyze the vehicle’s dynamic model, considering the vehicle body and in-wheel motors, to establish the foundation for model predictive control. Subsequently, we propose a model predictive direct motion control (MPDMC) approach that utilizes a single CPU to directly follow the driver’s commands by generating voltage references with a minimum cost function. The cost function of MPDMC is constructed, incorporating factors such as the longitudinal velocity, yaw rate, lateral displacement, and efficiency. We extensively analyze the weighting parameters of the cost function and introduce an optimization algorithm based on particle swarm optimization (PSO). This algorithm takes into account the aforementioned factors as well as the driving feeling, which is evaluated using a trained long short-term memory (LSTM) neural network. The LSTM network labels the response under different weighting parameters in various working conditions, i.e., “Nor”, “Eco”, and “Spt”. Finally, we evaluate the performance of the optimized MPDMC through simulations conducted using MATLAB and CarSim software. Four typical scenarios are considered, and the results demonstrate that the optimized MPDMC outperforms the baseline methods, achieving the best performance. Full article
(This article belongs to the Special Issue Integrated Control and Sensing Technology for Electric Vehicles)
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