16,496 research outputs found

    A state-of-the-art review on torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains

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    ยฉ 2019, Levrotto and Bella. All rights reserved. Electric vehicles are the future of private passenger transportation. However, there are still several technological barriers that hinder the large scale adoption of electric vehicles. In particular, their limited autonomy motivates studies on methods for improving the energy efficiency of electric vehicles so as to make them more attractive to the market. This paper provides a concise review on the current state-of-the-art of torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains (FEVIADs). Starting from the operating principles, which include the "control allocation" problem, the peculiarities of each proposed solution are illustrated. All the existing techniques are categorized based on a selection of parameters deemed relevant to provide a comprehensive overview and understanding of the topic. Finally, future concerns and research perspectives for FEVIAD are discussed

    Actuators for Intelligent Electric Vehicles

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    This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

    A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

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    Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

    ๊ณ ์„ฑ๋Šฅ ํ•œ๊ณ„ ํ•ธ๋“ค๋ง์„ ์œ„ํ•œ ์ธํœ ๋ชจํ„ฐ ํ† ํฌ๋ฒกํ„ฐ๋ง ์ œ์–ด

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2021.8. ์ด๊ฒฝ์ˆ˜.์ง€๋‚œ 10๋…„ ๋™์•ˆ ์ฐจ๋Ÿ‰ ์ž์„ธ ์ œ์–ด์‹œ์Šคํ…œ(ESC)์€ ์น˜๋ช…์ ์ธ ์ถฉ๋Œ์„ ๋ฐฉ์ง€ํ•˜๊ธฐ ์œ„ํ•ด ๋งŽ์€ ์ƒ์šฉ ์ฐจ๋Ÿ‰์—์„œ ๋น„์•ฝ์ ์œผ๋กœ ๋ฐœ์ „๋˜๊ณ  ๊ฐœ๋ฐœ๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, ์ฐจ๋Ÿ‰ ์ž์„ธ ์ œ์–ด ์‹œ์Šคํ…œ์€ ์•…์ฒœํ›„๋กœ ์ธํ•œ ๋ฏธ๋„๋Ÿฌ์šด ๋„๋กœ์™€ ๊ฐ™์€ ์œ„ํ—˜ํ•œ ๋„๋กœ์—์„œ ๋ถˆ์•ˆ์ •ํ•œ ์ฐจ๋Ÿ‰ ์ฃผํ–‰ ์กฐ๊ฑด์—์„œ ์‚ฌ๊ณ ๋ฅผ ํ”ผํ•˜๋Š”๋ฐ ํฐ ์—ญํ• ์„ ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์ตœ๊ทผ์˜ ๊ฒฝ์šฐ, ๊ณ ์„ฑ๋Šฅ ์ฐจ๋Ÿ‰ ๋˜๋Š” ์Šคํฌ์ธ ์นด ๋“ฑ์˜ ๊ฒฝ์šฐ ์ œ๋™์ œ์–ด์˜ ๋นˆ๋ฒˆํ•œ ๊ฐœ์ž…์€ ์šด์ „์˜ ์ฆ๊ฑฐ์›€์„ ๊ฐ์†Œ์‹œํ‚ค๋Š” ๋ถˆ๋งŒ๋„ ์กด์žฌํ•œ๋‹ค. ์ตœ๊ทผ ์ฐจ๋Ÿ‰์˜ ์ „๋™ํ™”์™€ ํ•จ๊ป˜, ์ž๋Ÿ‰ ์ž์„ธ ์ œ์–ด์‹œ์Šคํ…œ์˜ ์ž‘๋™ ์˜์—ญ์ธ ํ•œ๊ณ„ ์ฃผํ–‰ ํ•ธ๋“ค๋ง ์กฐ๊ฑด์—์„œ ๊ฐ ํœ ์˜ ๋…๋ฆฝ์ ์ธ ๊ตฌ๋™์„ ์ ์šฉ ํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ์Šคํ…œ ์ค‘ ํ•˜๋‚˜์ธ ์ธํœ  ๋ชจํ„ฐ ์‹œ์Šคํ…œ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ฐจ๋Ÿ‰์˜ ์ข…, ํšก๋ฐฉํ–ฅ ํŠน์„ฑ์„ ์ œ์–ด ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ํ† ํฌ ๋ฒกํ„ฐ๋ง ์ œ์–ด๊ธฐ์ˆ ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํ•˜๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ฐจ๋Ÿ‰์˜ ์„ ํšŒ ํ•œ๊ณ„ ํ•ธ๋“ค๋ง ์กฐ๊ฑด์—์„œ ์•ˆ์ •์„ฑ๊ณผ ์ฃผํ–‰ ๋‹ค์ด๋‚˜๋ฏน ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ํ† ํฌ ๋ฒกํ„ฐ๋ง ์ œ์–ด๊ธฐ๋ฅผ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋จผ์ €, ์ฐจ๋Ÿ‰์˜ ๋น„์„ ํ˜• ์ฃผํ–‰ ๊ตฌ๊ฐ„์ธ ํ•œ๊ณ„ ํ•ธ๋“ค๋ง ์กฐ๊ฑด์— ๋Œ€ํ•œ ์ž๋™ ๋“œ๋ฆฌํ”„ํŠธ ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜์—ฌ ํ† ํฌ๋ฒกํ„ฐ๋ง์ œ์–ด์— ์ฐจ๋Ÿ‰์˜ ๋‹ค์ด๋‚˜๋ฏนํ•œ ์ฃผํ–‰๋ชจ๋“œ์— ๋Œ€ํ•œ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•˜๊ณ  ๋ฏธ๋„๋Ÿฌ์šด ๋„๋กœ์—์„œ ์ฐจ๋Ÿ‰์˜ ๋†’์€ ์Šฌ๋ฆฝ ๊ฐ๋„์˜ ์•ˆ์ •์„ฑ ์ œ์–ด๋ฅผ ์ œ๊ณต ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ์ธํœ  ๋ชจํ„ฐ ์‹œ์Šคํ…œ์„ ์ฐจ๋Ÿ‰์˜ ์ „๋ฅœ์— 2๊ฐœ ๋ชจํ„ฐ๋กœ ์‚ฌ์šฉํ•˜์—ฌ ์ฐจ๋Ÿ‰ ๊ณ ์œ ์˜ ํŠน์„ฑ์ธ ์ฐจ๋Ÿ‰ ์–ธ๋”์Šคํ‹ฐ์–ด ๊ตฌ๋ฐฐ๋ฅผ ์ง์ ‘์  ์ œ์–ด๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ, ์ฐจ๋Ÿ‰์˜ ํ•ธ๋“ค๋ง ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œ์ผฐ๋‹ค. ์ œ์–ด๊ธฐ์˜ ์ฑ„ํ„ฐ๋ง ํšจ๊ณผ๋ฅผ ์ค„์ด๊ณ  ๋น ๋ฅธ ์‘๋‹ต์„ ์–ป๊ธฐ ์œ„ํ•ด ์ƒˆ๋กœ์šด ๊ณผ๋„ ๋งค๊ฐœ ๋ณ€์ˆ˜๊ฐ€ ์ด์šฉํ•˜์—ฌ ์ˆ˜์‹ํ™”ํ•˜์—ฌ ๊ตฌ์„ฑํ•˜์˜€์œผ๋ฉฐ, ์ฐจ๋Ÿ‰์˜ ์ •์ƒ ์ƒํƒœ ๋ฐ ๊ณผ๋„ ํŠน์„ฑ ํ–ฅ์ƒ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ISO ๊ธฐ๋ฐ˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ ์ฐจ๋Ÿ‰ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์š” ์ œ์–ด๊ธฐ์™€ ํšก ์Šฌ๋ฆฝ ๊ฐ๋„ ์ œ์–ด๊ธฐ๋กœ ๊ตฌ์„ฑ๋œ MASMC (Multiple Adaptive Sliding Mode Control) ์ ‘๊ทผ ๋ฐฉ์‹์„ ์‚ฌ์šฉํ•˜๋Š” 4๋ฅœ ๋ชจํ„ฐ ์‹œ์Šคํ…œ์„ ์‚ฌ์šฉํ•œ ๋™์  ํ† ํฌ๋ฒกํ„ฐ๋ง ์ œ์–ด๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋†’์€ ๋น„์„ ํ˜• ํŠน์„ฑ์„ ๊ฐ€์ง„ ์ฐจ๋Ÿ‰์˜ ์ „ํ›„๋ฅœ ํƒ€์ด์–ด์˜ ์ฝ”๋„ˆ๋ง ๊ฐ•์„ฑ์€ ์ ์‘์ œ์–ด๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์˜ˆ์ธกํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ, ์•ˆ์ „๋ชจ๋“œ์™€ ๋‹ค์ด๋‚˜๋ฏน ๋ชจ๋“œ๋ฅผ ๊ตฌ์„ฑํ•˜์—ฌ, ์šด์ „์ž๋กœ ํ•˜์—ฌ๊ธˆ ์›ํ•˜๋Š” ์ฃผํ–‰์˜ ์กฐ๊ฑด์— ๋งž๊ฒŒ ์„ ํƒํ•  ์ˆ˜ ์žˆ๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ์ด MASMC ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ํ–ฅํ›„ ์ „๋™ํ™” ์ฐจ๋Ÿ‰์— ์ฃผํ–‰์•ˆ์ •์„ฑ ํ–ฅ์ƒ๊ณผ ๋‹ค์ด๋‚˜๋ฏนํ•œ ์ฃผํ–‰์˜ ์ฆ๊ฑฐ์›€์„ ์ฃผ๋Š” ๊ธฐ์ˆ ๋กœ์จ, ์ „์ฐจ๋Ÿ‰ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ด์šฉํ•˜์—ฌ ๊ฒ€์ฆํ•˜์˜€๋‹ค.In the last ten decades, vehicle stability control systems have been dramatically developed and adapted in many commercial vehicles to avoid fatal crashes. Significantly, ESC (Electric Stability Control) system can help escape the accident from unstable driving conditions with dangerous roads such as slippery roads due to inclement weather conditions. However, for the high performed vehicle, frequent intervention from ESC reduces the pleasure of fun-to-drive. Recently, the development of traction control technologies has been taking place with that of the electrification of vehicles. The IWMs (In-Wheel Motor system), which is one of the systems that can apply independent drive of each wheel, for the limit handling characteristics, which are the operation areas of the ESC, is introduced for the control that enables the lateral characteristics of the vehicle dynamics. Firstly, the automated drift control algorithm can be proposed for the nonlinear limit handling condition of vehicles. This approach can give an insight of fun-to-drive mode to TV (Torque Vector) control scheme, but also the stability control of high sideslip angle of the vehicle on slippery roads. Secondly, using IWMs system with front two motors, understeer gradient of vehicle, which is the unique characteristics of vehicle can be used for the proposed control strategy. A new transient parameter is formulated to be acquired rapid response of controller and reducing chattering effects. Simulation and vehicle tests are conducted for validation of TV control algorithm with steady-state and transient ISO-based tests. Finally, dynamic torque vectoring control with a four-wheel motor system with Multiple Adaptive Sliding Mode Control (MASMC) approach, which is composed of a yaw rate controller and sideslip angle controller, is introduced. Highly nonlinear characteristics, cornering stiffnesses of front and rear tires are estimated by adaptation law with measuring data. Consequently, there are two types of driving modes, the safety mode and the dynamic mode. MASMC algorithm can be found and validated by simulation in torque vectoring technology to improve the handling performance of fully electric vehicles.Chapter 1 Introduction 7 1.1. Background and Motivation 7 1.2. Literature review 11 1.3. Thesis Objectives 15 1.4. Thesis Outline 15 Chapter 2 Vehicle dynamic control at limit handling 17 2.1. Vehicle Model and Analysis 17 2.1.1. Lateral dynamics of vehicle 17 2.1.2. Longitudinal dynamics of vehicle 20 2.2. Tire Model 24 2.3. Analysis of vehicle drift for fun-to-drive 28 2.4. Designing A Controller for Automated Drift 34 2.4.1. Lateral controller 35 2.4.2. Longitudinal Controller 37 2.4.3. Stability Analysis 39 2.4.4. Validation with simulation and test 40 Chapter 3 Torque Vectoring Control with Front Two Motor In-Wheel Vehicles 47 3.1. Dynamic Torque Vectoring Control 48 3.1.1. In-wheel motor system (IWMs) 48 3.1.2. Dynamic system modeling 49 3.1.3. Designing controller 53 3.2. Validation with Simulation and Experiment 59 3.2.1. Simulation 59 3.2.2. Vehicle Experiment 64 Chapter 4 Dynamic handling control for Four-wheel Drive In-Wheel platform 75 4.1. Vehicle System Modeling 76 4.2. Motion Control based on MASMC 78 4.2.1. Yaw motion controller for the inner ASMC 80 4.2.2. Sideslip angle controller for the outer ASMC 84 4.3. Optimal Torque Distribution (OTD) 88 4.3.1. Constraints of dynamics 88 4.3.2. Optimal torque distribution law 90 4.4. Validation with Simulation 91 4.4.1. Simulation setup 91 4.4.2. Simulation results 92 Chapter 5 Conclusion and Future works 104 5.1 Conclusion 104 5.2 Future works 106 Bibliography 108 Abstract in Korean 114๋ฐ•

    A Convex Approach to Path Tracking with Obstacle Avoidance for Pseudo-Omnidirectional Vehicles

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    This report addresses the related problems of trajectory generation and time-optimal path tracking with online obstacle avoidance. We consider the class of four-wheeled vehicles with independent steering and driving on each wheel, also referred to as pseudo-omnidirectional vehicles. Appropriate approximations of the dynamic model enable a convex reformulation of the path-tracking problem. Using the precomputed trajectories together with model predictive control that utilizes feedback from the estimated global pose, provides robustness to model uncertainty and disturbances. The considered approach also incorporates avoidance of a priori unknown moving obstacles by local online replanning. We verify the approach by successful execution on a pseudo-omnidirectional mobile robot, and compare it to an existing algorithm. The result is a significant decrease in the time for completing the desired path. In addition, the method allows a smooth velocity trajectory while avoiding intermittent stops in the path execution
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