798 research outputs found

    Control of an Independent 4WD electric vehicle by DYC method

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    Current advances in the application of control systems to vehicle dynamics has made it practicable to improve the vehicleโ€™s longitudinal, lateral and vertical dynamics. Some of the examples of application of these systems to vehicle control are traction control (longitudinal dynamics) to prevent wheel slip, ESP (lateral dynamics) to prevent loss of stability, and active suspension (vertical dynamics) to increase ride comfort. In this paper, the vehicle lateral motion is controlled by direct yaw control (DYC) method. This uses the yaw moment produced by the longitudinal forces of the tyres, for stabilising the vehicle motion during critical cornering conditions. The system is been designed to give substantially enhanced active safety and dynamic handling control. The vehicle dynamics control algorithm is developed for a FOX vehicle by controlling couple traction/braking torque of the four in-wheel motors, from basic driving slogans. These are the steering angle, position of the accelerator pedal and brake by the position of the brake pedal, as shown in Figure 1

    A comprehensive study of key Electric Vehicle (EV) components, technologies, challenges, impacts, and future direction of development

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    Abstract: Electric vehicles (EV), including Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV), Plug-in Hybrid Electric Vehicle (PHEV), Fuel Cell Electric Vehicle (FCEV), are becoming more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is likely to replace internal combustion engine (ICE) vehicles in the near future. Each of the main EV components has a number of technologies that are currently in use or can become prominent in the future. EVs can cause significant impacts on the environment, power system, and other related sectors. The present power system could face huge instabilities with enough EV penetration, but with proper management and coordination, EVs can be turned into a major contributor to the successful implementation of the smart grid concept. There are possibilities of immense environmental benefits as well, as the EVs can extensively reduce the greenhouse gas emissions produced by the transportation sector. However, there are some major obstacles for EVs to overcome before totally replacing ICE vehicles. This paper is focused on reviewing all the useful data available on EV configurations, battery energy sources, electrical machines, charging techniques, optimization techniques, impacts, trends, and possible directions of future developments. Its objective is to provide an overall picture of the current EV technology and ways of future development to assist in future researches in this sector

    ๊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•

    Electric Vehicle Efficient Power and Propulsion Systems

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    Vehicle electrification has been identified as one of the main technology trends in this second decade of the 21st century. Nearly 10% of global car sales in 2021 were electric, and this figure would be 50% by 2030 to reduce the oil import dependency and transport emissions in line with countriesโ€™ climate goals. This book addresses the efficient power and propulsion systems which cover essential topics for research and development on EVs, HEVs and fuel cell electric vehicles (FCEV), including: Energy storage systems (battery, fuel cell, supercapacitors, and their hybrid systems); Power electronics devices and converters; Electric machine drive control, optimization, and design; Energy system advanced management methods Primarily intended for professionals and advanced students who are working on EV/HEV/FCEV power and propulsion systems, this edited book surveys state of the art novel control/optimization techniques for different components, as well as for vehicle as a whole system. New readers may also find valuable information on the structure and methodologies in such an interdisciplinary field. Contributed by experienced authors from different research laboratory around the world, these 11 chapters provide balanced materials from theorical background to methodologies and practical implementation to deal with various issues of this challenging technology. This reprint encourages researchers working in this field to stay actualized on the latest developments on electric vehicle efficient power and propulsion systems, for road and rail, both manned and unmanned vehicles

    A Review of Active Yaw Control System for Vehicle Handling and Stability Enhancement

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    Yaw stability control systemplays a significant role in vehicle lateral dynamics in order to improve the vehicle handling and stability performances. However, not many researches have been focused on the transient performances improvement of vehicle yaw rate and sideslip tracking control. This paper reviews the vital elements for control system design of an active yaw stability control system; the vehicle dynamic models, control objectives, active chassis control, and control strategies with the focus on identifying suitable criteria for improved transient performances. Each element is discussed and compared in terms of their underlying theory, strengths, weaknesses, and applicability. Based on this, we conclude that the sliding mode control with nonlinear sliding surface based on composite nonlinear feedback is a potential control strategy for improving the transient performances of yaw rate and sideslip tracking control

    Parameter tuning and cooperative control for automated guided vehicles

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    For several practical control engineering applications it is desirable that multiple systems can operate independently as well as in cooperation with each other. Especially when the transition between individual and cooperative behavior and vice versa can be carried out easily, this results in ??exible and scalable systems. A subclass is formed by systems that are physically separated during individual operation, and very tightly coupled during cooperative operation. One particular application of multiple systems that can operate independently as well as in concert with each other is the cooperative transportation of a large object by multiple Automated Guided Vehicles (AGVs). AGVs are used in industry to transport all kinds of goods, ranging from small trays of compact and video discs to pallets and 40-tonne coils of steel. Current applications typically comprise a ??eet of AGVs, and the vehicles transport products on an individual basis. Recently there has been an increasing demand to transport very large objects such as sewer pipes, rotor blades of wind turbines and pieces of scenery for theaters, which may reach lengths of over thirty meters. A realistic option is to let several AGVs operate together to handle these types of loads. This Ph.D. thesis describes the development, implementation, and testing of distributed control algorithms for transporting a load by two or more Automated Guided Vehicles in industrial environments. We focused on the situations where the load is connected to the AGVs by means of (semi-)rigid interconnections. Attention was restricted to control on the velocity level, which we regard as an intermediate step for achieving fully automatic operation. In our setup the motion setpoint is provided by an external host. The load is assumed to be already present on the vehicles. Docking and grasping procedures are not considered. The project is a collaboration between the company FROG Navigation Systems (Utrecht, The Netherlands) and the Control Systems group of the Technische Universiteit Eindhoven. FROG provided testing facilities including two omni-directional AGVs. Industrial AGVs are custom made for the transportation tasks at hand and come in a variety of forms. To reduce development times it is desirable to follow a model-based control design approach as this allows generalization to a broad class of vehicles. We have adopted rigid body modeling techniques from the ??eld of robotic manipulators to derive the equations of motion for the AGVs and load in a systematic way. These models are based on physical considerations such as Newton's second law and the positions and dimensions of the wheels, sensors, and actuators. Special emphasis is put on the modeling of the wheel-??oor interaction, for which we have adopted tire models that stem from the ??eld of vehicle dynamics. The resulting models have a clear physical interpretation and capture a large class of vehicles with arbitrary wheel con??gurations. This ensures us that the controllers, which are based on these models, are applicable to a broad class of vehicles. An important prerequisite for achieving smooth cooperative behavior is that the individual AGVs operate at the required accuracy. The performance of an individual AGV is directly related to the precision of the estimates for the odometric parameters, i.e. the effective wheel diameters and the offsets of the encoders that measure the steering angles of the wheels. Cooperative transportation applications will typically require AGVs that are highly maneuverable, which means that all the wheels of an individual AGV ahould be able to steer. Since there will be more than one steering angle encoder, the identi??cation of the odometric parameters is substantially more dif??cult for these omni-directional AGVs than for the mobile wheeled robots that are commonly seen in literature and laboratory settings. In this thesis we present a novel procedure for simultaneously estimating effective wheel diameters and steering angle encoder offsets by driving several pure circle segments. The validity of the tuning procedure is con??rmed by experiments with the two omni-directional test vehicles with varying loads. An interesting result is that the effective wheel diameters of the rubber wheels of our AGVs increase with increasing load. A crucial aspect in all control designs is the reconstruction of the to-be-controlled variables from measurement data. Our to-be-controlled variables are the planar motion of the load and the motions of the AGVs with respect to the load, which have to be reconstruct from the odometric sensor information. The odometric sensor information consists of the drive encoder and steering encoder readings. We analyzed the observability of an individual AGV and proved that it is theoretically possible to reconstruct its complete motion from the odometric measurements. Due to practical considerations, we pursued a more pragmatic least-squares based observer design. We show that the least-squares based motion estimate is independent of the coordinate system that is being used. The motion estimator was subsequently analyzed in a stochastic setting. The relation between the motion estimator and the estimated velocity of an arbitrary point on the vehicle was explored. We derived how the covariance of the velocity estimate of an arbitrary point on the vehicle is related to the covariance of the motion estimate. We proved that there is one unique point on the vehicle for which the covariance of the estimated velocity is minimal. Next, we investigated how the local motion estimates of the individual AGVs can be combined to yield one global estimate. When the load and AGVs are rigidly interconnected, it suf??ces that each AGVs broadcasts its local motion estimate and receives the estimates of the other AGVs. When the load is semi-rigidly interconnected to the AGVs, e.g. by means of revolute or prismatic joints, then generally each AGV needs to broadcasts the corresponding information matrix as well. We showed that the information matrix remains constant when the load is connected to the AGV with a revolute joint that is mounted at the aforementioned unique point with the smallest velocity estimate covariance. This means that the corresponding AGV does not have to broadcast its information matrix for this special situation. The key issue in the control design for cooperative transportation tasks is that the various AGVs must not counteract each others' actions. The decentralized controller that we derived makes the AGVs track an externally provided planar motion setpoint while minimizing the interconnection forces between the load and the vehicles. Although the control design is applicable to cooperative transportation by multiple AGVs with arbitrary semi-rigid AGV-load interconnections, it is noteworthy that a particularly elegant solution arises when all interconnections are completely rigid. Then the derived local controllers have the same structure as the controllers that are normally used for individual operation. As a result, changing a few parameter settings and providing the AGVs with identical setpoints is all that is required to achieve cooperative behavior on the velocity level for this situation. The observer and controller designs for the case that the AGVs are completely rigidly interconnected to the load were successfully implemented on the two test vehicles. Experi ments were carried out with and without a load that consisted of a pallet with 300 kg pave stones. The results were reproducible and illustrated the practical validity of the observer and controller designs. There were no substantial drawbacks when the local observers used only their local sensor information, which means that our setup can also operate satisfactory when the velocity estimates are not shared with the other vehicles
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