Advances in Intelligent Vehicle Control Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 2735

Special Issue Editors


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Guest Editor
School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
Interests: vehicle dynamics control; intelligent control of electric vehicles; autonomous vehicles; optimal control

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Guest Editor
School of Mechanical Engineering, National Engineering Research Center for Electric Vehicles, Beijing Institute of Technology, Beijing, China
Interests: distributed drive and control; design and management for powertrain systems; thermal and safety management for batteries; intelligent drive of connected vehicles
Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, China
Interests: optimal control of hybrid electric vehicles; state estimation and management for batteries; optimal control of connected vehicles; motor drive control

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Guest Editor
School of Mechanical and Vehicular Engineering, Hunan University, Changsha, China
Interests: intelligent control of electric vehicles; energy management; modelling and control for battery; automotive functional safety

Special Issue Information

Dear Colleagues,

The era of intelligent vehicles has come. Superior vehicle intelligence is promising to improve drive safety, road utilization, energy efficiency, and environmental friendliness, leading to significant revolutions in industry and daily life. However, to make these happen, research communities still need to address several challenges. A vehicle system is a multi-component coupling and highly nonlinear device, leading to huge difficulties in improving the effects of modelling, sub-system state estimation, and control. Perception information about surrounding environments, drive conditions, and vehicle states is non-negligible for intelligent vehicles, but many challenges are still to be addressed, especially under off-road driving conditions. The increasing utilization rate of intelligent vehicles, as well as vehicle-to-vehicle/vehicle-to-cloud communication, also raise demands for the development of timely fault diagnosis, effective fault-tolerance schemes, reliable information selection, the framework applicability of strategies, etc.

This Special Issue encourages researchers working in this field to share their latest developments on perception, estimation, decision making, motion planning, control, design, model, simulation, diagnosis and fault-tolerance, and application which are relevant for intelligent vehicle control systems.

The topics of interest include, but are not confined to:

  • Perception and estimation technologies: sensor fusion techniques for drive environments, state estimation for intelligent systems (i.e., information for components and vehicular networks), etc.
  • Decision making and motion planning: novel motion planning frameworks and technologies, decision making and trajectory planning under challenging environments (e.g., indoor, outdoor, structured, and unstructured etc.).
  • Advanced design in powertrain and chassis configuration: novel propulsion architectures and highly efficient sizing methods to enhance energy efficiency, maneuverability, and functionality.
  • Control algorithm: advanced control regarding vehicular sub-systems (battery, fuel cell, motor etc.), powertrain, vehicle dynamics, queue, and formation, to improve the effects in energy efficiency, maneuverability, handling stability, trajectory tracking, and their coordination.
  • Connectivity exploitation: construction of application scenes, data-driven parameters sizing, vehicular control, and topology development under drive conditions of vehicle-to-vehicle/vehicle-to-cloud communication.
  • Modelling and simulation techniques: high-accuracy modelling and accelerated simulation methods for function assessments, including field testing development and those with digital-twin techniques.
  • Artificial intelligence techniques: theory and applications using machine learning to address issues in improving training convergence, robustness, stability, and acceptation-oriented standardization etc., for various applications in intelligent vehicle control.
  • Diagnosis and fault-tolerance control: diagnosis and fault-tolerance mechanisms/approaches to ensure operations for safety-critical vehicle systems (sub-systems, actuators, sensors etc.)
  • Application cases: application illustrations of intelligent vehicle control with favorable reference value, for cars, tracked vehicles, off-road vehicles, e-bikes, buses, heavy-duty vehicles and so forth.

Dr. Ningyuan Guo
Prof. Dr. Junqiu Li
Dr. Zheng Chen
Dr. Wei Zhou
Guest Editors

Manuscript Submission Information

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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. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • perception and estimation technologies
  • decision making and motion planning
  • advanced design in powertrain and chassis configuration
  • control algorithms
  • connectivity exploitation
  • modelling and simulation techniques
  • artificial intelligence techniques
  • diagnosis and fault-tolerant control
  • application cases

Published Papers (2 papers)

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Research

26 pages, 10416 KiB  
Article
Multi-Objective Collaborative Control Method for Multi-Axle Distributed Vehicle Assisted Driving
by Weichen Wang, Junqiu Li, Xiaohan Li, Zhichao Li and Ningyuan Guo
Appl. Sci. 2023, 13(13), 7769; https://doi.org/10.3390/app13137769 - 30 Jun 2023
Cited by 1 | Viewed by 886
Abstract
For human–machine collaborative driving conditions, a hierarchical chassis multi-objective cooperative control method is proposed in this paper. Firstly, based on the phase plane theory, vehicle dynamics analysis is carried out to complete the definition of vehicle stability region. Secondly, based on the linear [...] Read more.
For human–machine collaborative driving conditions, a hierarchical chassis multi-objective cooperative control method is proposed in this paper. Firstly, based on the phase plane theory, vehicle dynamics analysis is carried out to complete the definition of vehicle stability region. Secondly, based on the linear time-varying (LTV) system model, a cooperative control strategy combining fuzzy control with model predictive control (MPC) is proposed in the upper layer. In this strategy, the assisted driving weight adjustment coefficient and the stability weight adjustment coefficient are obtained by fuzzy mapping combining human–machine cooperation index and the vehicle stability region, respectively, and the optimization objectives of MPC are designed based on the above coefficients. In the lower layer torque allocation strategy, the stability weight adjustment coefficient is introduced to achieve multi-objective optimization of tire load rate and energy efficiency. For energy efficiency optimization, an optimal energy efficiency point-based tracking method is proposed to avoid nonlinearity caused by the introduction of motor loss models. Simulation analysis results show that the proposed strategy can effectively alleviate human–machine conflicts and improve vehicle handing stability. It also can achieve smaller tire load rate optimization through torque allocation and can reduce energy consumption by approximately 8% compared with the inter-axle torque allocation strategy. This study helps to promote the improvement of the comprehensive performance of assisted driving vehicles in human–machine cooperation, handling stability, and energy-saving torque distribution. Full article
(This article belongs to the Special Issue Advances in Intelligent Vehicle Control Systems)
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17 pages, 4913 KiB  
Article
Study on Correction Method of Internal Joint Operation Curve Based on Unsteady Flow
by Sheng Yin, Jimin Ni, Houchuan Fan, Xiuyong Shi and Rong Huang
Appl. Sci. 2022, 12(23), 11943; https://doi.org/10.3390/app122311943 - 23 Nov 2022
Cited by 1 | Viewed by 1117
Abstract
The turbocharger, a key component in a vehicle’s powertrain, results in insufficient accuracy if it does not fully consider the unsteady flow effects of the intake and exhaust systems. Based on the difference between the turbocharger’s actual operating performance with unsteady flow and [...] Read more.
The turbocharger, a key component in a vehicle’s powertrain, results in insufficient accuracy if it does not fully consider the unsteady flow effects of the intake and exhaust systems. Based on the difference between the turbocharger’s actual operating performance with unsteady flow and the corresponding steady flow performance, unsteady flow correction concepts and correction methods for the compressor and turbine were put forward, and the correction of the internal joint operation curve was investigated. The results show that when unsteady correction coefficients were added to both ends of the turbocharger and the optimized structure was used at both ends, the original turbocharger’s surge margin was reduced by 4.6% to 11.8%, and that of the optimized turbocharger was reduced by 15.2% to 21.9% in the medium–low-speed range. Meanwhile, the unsteady flow energy utilization coefficient of the optimized turbocharger was more than 14.5% higher than that of the original turbocharger in the medium–low speed range, and the energy utilization advantage was obvious. It indicated that the optimized turbocharger was working earlier, and the engine’s medium–low-speed admission performance has been obviously improved. Therefore, compared with the steady curve, the corrected unsteady curve was closer to the actual engine performance. Full article
(This article belongs to the Special Issue Advances in Intelligent Vehicle Control Systems)
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