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Integrated Control and Sensing Technology for 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 (31 October 2024) | Viewed by 17482

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Special Issue Editors

Dr. Yuanjian Zhang

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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
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Dr. Jingjing Jiang

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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
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Dr. Ricardo De Castro

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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

Dr. Ji Li

E-Mail Website
Guest Editor

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

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Published Papers (12 papers)

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Research

23 pages, 9502 KiB

Open AccessArticle

Energy-Oriented Hybrid Cooperative Adaptive Cruise Control for Fuel Cell Electric Vehicle Platoons

by Shibo Li, Liang Chu, Pengyu Fu, Shilin Pu, Yilin Wang, Jinwei Li and Zhiqi Guo

Sensors 2024, 24(15), 5065; https://doi.org/10.3390/s24155065 - 5 Aug 2024

Viewed by 982

Abstract

Given the complex powertrain of fuel cell electric vehicles (FCEVs) and diversified vehicle platooning synergy constraints, a control strategy that simultaneously considers inter-vehicle synergy control and energy economy is one of the key technologies to improve transportation efficiency and release the energy-saving potential of platooning vehicles. In this paper, an energy-oriented hybrid cooperative adaptive cruise control (eHCACC) strategy is proposed for an FCEV platoon, aiming to enhance energy-saving potential while ensuring stable car-following performance. The eHCACC employs a hybrid cooperative control architecture, consisting of a top-level centralized controller (TCC) and bottom-level distributed controllers (BDCs). The TCC integrates an eco-driving CACC (eCACC) strategy based on the minimum principle and random forest, which generates optimal reference velocity datasets by aligning the comprehensive control objectives of the platoon and addressing the car-following performance and economic efficiency of the platoon. Concurrently, to further unleash energy-saving potential, the BDCs utilize the equivalent consumption minimization strategy (ECMS) to determine optimal powertrain control inputs by combining the reference datasets with detailed optimization information and system states of the powertrain components. A series of simulation evaluations highlight the improved car-following stability and energy efficiency of the FCEV platoon. Full article

(This article belongs to the Special Issue Integrated Control and Sensing Technology for Electric Vehicles)

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23 pages, 3504 KiB

Open AccessArticle

Distributed Drive Electric Vehicle Handling Stability Coordination Control Framework Based on Adaptive Model Predictive Control

by Jianhua Guo, Zhiyuan Dai, Ming Liu, Zhihao Xie, Yu Jiang, Haochun Yang and Dong Xie

Sensors 2024, 24(15), 4811; https://doi.org/10.3390/s24154811 - 24 Jul 2024

Viewed by 888

Abstract

Distributed drive electric vehicles improve steering response and enhance overall vehicle stability by independently controlling each motor. This paper introduces a control framework based on Adaptive Model Predictive Control (AMPC) for coordinating handling stability, consisting of three layers: the dynamic supervision layer, online [...] Read more.

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phase plane stability boundary and designs variable weight factors based on this stability boundary. The online optimization layer constructs the target weight-adaptive AMPC strategy, which can adjust the control weights for maneuverability and lateral stability in real time based on the variable weight factors provided by the dynamic supervision layer. The low-level control layer precisely allocates the driver’s requested driving force and additional yaw moment by using torque distribution error and tire utilization as the cost function. Finally, experiments are conducted on a Simulink-CarSim co-simulation platform to assess the performance of AMPC. Simulation results show that, compared to the traditional MPC strategy, this control strategy not only enhances maneuverability under normal conditions but also improves lateral stability control under extreme conditions. Full article

(This article belongs to the Special Issue Integrated Control and Sensing Technology for Electric Vehicles)

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17 pages, 10474 KiB

Open AccessArticle

Spatio-Temporal Joint Optimization-Based Trajectory Planning Method for Autonomous Vehicles in Complex Urban Environments

by Jianhua Guo, Zhihao Xie, Ming Liu, Zhiyuan Dai, Yu Jiang, Jinqiu Guo and Dong Xie

Sensors 2024, 24(14), 4685; https://doi.org/10.3390/s24144685 - 19 Jul 2024

Viewed by 1192

Abstract

Providing safe, smooth, and efficient trajectories for autonomous vehicles has long been a question of great interest in the field of autopiloting. In dynamic and ever-changing urban environments, safe and efficient trajectory planning is fundamental to achieving autonomous driving. Nevertheless, the complexity of environments with multiple constraints poses challenges for trajectory planning. It is possible that behavior planners may not successfully obtain collision-free trajectories in complex urban environments. Herein, this paper introduces spatio–temporal joint optimization-based trajectory planning (SJOTP) with multi-constraints for complex urban environments. The behavior planner generates initial trajectory clusters based on the current state of the vehicle, and a topology-guided hybrid A* algorithm applied to an inflated map is utilized to address the risk of collisions between the initial trajectories and static obstacles. Taking into consideration obstacles, road surface adhesion coefficients, and vehicle dynamics constraints, multi-constraint multi-objective coordinated trajectory planning is conducted, using both differential-flatness vehicle models and point-mass vehicle models. Taking into consideration longitudinal and lateral coupling in trajectory optimization, a spatio–temporal joint optimization solver is used to obtain the optimal trajectory. The simulation verification was conducted on a multi-agent simulation platform. The results demonstrate that this methodology can obtain optimal trajectories safely and efficiently in complex urban environments. Full article

(This article belongs to the Special Issue Integrated Control and Sensing Technology for Electric Vehicles)

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30 pages, 31803 KiB

Open AccessArticle

An NMPC-Based Integrated Longitudinal and Lateral Vehicle Stability Control Based on the Double-Layer Torque Distribution

by Xu Bai, Yinhang Wang, Mingchen Jia, Xinchen Tan, Liqing Zhou, Liang Chu and Di Zhao

Sensors 2024, 24(13), 4137; https://doi.org/10.3390/s24134137 - 26 Jun 2024

Viewed by 1230

Abstract

With the ongoing promotion and adoption of electric vehicles, intelligent and connected technologies have been continuously advancing. Electrical control systems implemented in electric vehicles have emerged as a critical research direction. Various drive-by-wire chassis systems, including drive-by-wire driving and braking systems and steer-by-wire systems, are extensively employed in vehicles. Concurrently, unavoidable issues such as conflicting control system objectives and execution system interference emerge, positioning integrated chassis control as an effective solution to these challenges. This paper proposes a model predictive control-based longitudinal dynamics integrated chassis control system for pure electric commercial vehicles equipped with electro–mechanical brake (EMB) systems, centralized drive, and distributed braking. This system integrates acceleration slip regulation (ASR), a braking force distribution system, an anti-lock braking system (ABS), and a direct yaw moment control system (DYC). This paper first analyzes and models the key components of the vehicle. Then, based on model predictive control (MPC), it develops a controller model for integrated stability with double-layer torque distribution. The required driving and braking torque for each wheel are calculated according to the actual and desired motion states of the vehicle and applied to the corresponding actuators. Finally, the effectiveness of this strategy is verified through simulation results from Matlab/Simulink. The simulation shows that the braking deceleration of the braking condition is increased by 32% on average, and the braking distance is reduced by 15%. The driving condition can enter the smooth driving faster, and the time is reduced by 1.5 s~5 s. The lateral stability parameters are also very much improved compared with the uncontrolled vehicles. Full article

(This article belongs to the Special Issue Integrated Control and Sensing Technology for Electric Vehicles)

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20 pages, 13727 KiB

Open AccessArticle

Design and Implementation of a Low-Cost Intelligent Unmanned Surface Vehicle

by Piyabhum Chaysri, Christos Spatharis, Kostas Vlachos and Konstantinos Blekas

Sensors 2024, 24(10), 3254; https://doi.org/10.3390/s24103254 - 20 May 2024

Cited by 1 | Viewed by 1287

Abstract

This article describes the design and construction journey of a self-developed unmanned surface vehicle (USV). In order to increase the accessibility and lower the barrier of entry we propose a low-cost (under EUR 1000) approach to the vessel construction with great adaptability and customizability. This design prioritizes minimal power consumption as a key objective. It focuses on elucidating the intricacies of both the design and assembly processes involved in creating an economical USV. Utilizing easily accessible components, the boat outlined in this study has been already participated in the 1st Aegean Ro-boat Race 2023 competition and is tailored for entry into similar robotic competitions. Its primary functionalities encompass autonomous sea navigation coupled with sophisticated collision avoidance capabilities. Finally, we studied reinforcement learning strategies for constructing a robust intelligent controller for the task of USV navigation under disturbances and we show some preliminary simulation results we have obtained. Full article

(This article belongs to the Special Issue Integrated Control and Sensing Technology for Electric Vehicles)

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25 pages, 7725 KiB

Open AccessArticle

Intelligent Vehicle Path Planning Based on Optimized A* Algorithm

by Liang Chu, Yilin Wang, Shibo Li, Zhiqi Guo, Weiming Du, Jinwei Li and Zewei Jiang

Sensors 2024, 24(10), 3149; https://doi.org/10.3390/s24103149 - 15 May 2024

Viewed by 2240

Abstract

With the rapid development of the intelligent driving technology, achieving accurate path planning for unmanned vehicles has become increasingly crucial. However, path planning algorithms face challenges when dealing with complex and ever-changing road conditions. In this paper, aiming at improving the accuracy and robustness of the generated path, a global programming algorithm based on optimization is proposed, while maintaining the efficiency of the traditional A* algorithm. Firstly, turning penalty function and obstacle raster coefficient are integrated into the search cost function to increase the adaptability and directionality of the search path to the map. Secondly, an efficient search strategy is proposed to solve the problem that trajectories will pass through sparse obstacles while reducing spatial complexity. Thirdly, a redundant node elimination strategy based on discrete smoothing optimization effectively reduces the total length of control points and paths, and greatly reduces the difficulty of subsequent trajectory optimization. Finally, the simulation results, based on real map rasterization, highlight the advanced performance of the path planning and the comparison among the baselines and the proposed strategy showcases that the optimized A* algorithm significantly enhances the security and rationality of the planned path. Notably, it reduces the number of traversed nodes by 84%, the total turning angle by 39%, and shortens the overall path length to a certain extent. Full article

(This article belongs to the Special Issue Integrated Control and Sensing Technology for Electric Vehicles)

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28 pages, 7949 KiB

Open AccessArticle

RBS and ABS Coordinated Control Strategy Based on Explicit Model Predictive Control

by Liang Chu, Jinwei Li, Zhiqi Guo, Zewei Jiang, Shibo Li, Weiming Du, Yilin Wang and Chong Guo

Sensors 2024, 24(10), 3076; https://doi.org/10.3390/s24103076 - 12 May 2024

Viewed by 1284

Abstract

During the braking process of electric vehicles, both the regenerative braking system (RBS) and anti-lock braking system (ABS) modulate the hydraulic braking force, leading to control conflict that impacts the effectiveness and real-time capability of coordinated control. Aiming to enhance the coordinated control effectiveness of RBS and ABS within the electro-hydraulic composite braking system, this paper proposes a coordinated control strategy based on explicit model predictive control (eMPC-CCS). Initially, a comprehensive braking control framework is established, combining offline adaptive control law generation, online optimized control law application, and state compensation to effectively coordinate braking force through the electro-hydraulic system. During offline processing, eMPC generates a real-time-oriented state feedback control law based on real-world micro trip segments, improving the adaptiveness of the braking strategy across different driving conditions. In the online implementation, the developed three-dimensional eMPC control laws, corresponding to current driving conditions, are invoked, thereby enhancing the potential for real-time braking strategy implementation. Moreover, the state error compensator is integrated into eMPC-CCS, yielding a state gain matrix that optimizes the vehicle braking status and ensures robustness across diverse braking conditions. Lastly, simulation evaluation and hardware-in-the-loop (HIL) testing manifest that the proposed eMPC-CCS effectively coordinates the regenerative and hydraulic braking systems, outperforming other CCSs in terms of braking energy recovery and real-time capability. Full article

(This article belongs to the Special Issue Integrated Control and Sensing Technology for Electric Vehicles)

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20 pages, 4317 KiB

Open AccessArticle

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 - 7 Apr 2024

Cited by 1 | Viewed by 1236

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1173

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) 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

Open AccessArticle

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 1290

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 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

Open AccessArticle

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 1981

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. 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

Open AccessArticle

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

Cited by 3 | Viewed by 1375

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, 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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