Active Differential Control for Improved Handling Performance of Front-Wheel-Drive High-Performance Vehicles

Woo, Seunghee; Cha, Hyunsoo; Yi, Kyongsu; Jang, Seongyun

doi:10.1007/s12239-021-0050-2

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…Therefore, the exponential decay model is used in defining (13) in order to reflect the actuation delay in the prediction model. The exponential decay model is represented as (14) [60]. In (14), k a and k γ are the decay rate for the longitudinal acceleration and yaw rate, respectively.…”

Section: Reference State Decisionmentioning

confidence: 99%

“…The exponential decay model is represented as (14) [60]. In (14), k a and k γ are the decay rate for the longitudinal acceleration and yaw rate, respectively. The values of k a and k γ are set to 0.05 and 0.15 for state prediction, respectively.…”

Section: Reference State Decisionmentioning

confidence: 99%

“…In the area of vehicle chassis control, several actuators have been adopted to improve maneuverability or lateral stability [11,12]. For example, electronic stability control (ESC) with differential braking, torque vectoring devices, electronic-limited slip differential (e-LSD), active suspension, and rear-wheel steering (RWS) have been adopted as an actuator for vehicle chassis control [11][12][13][14]. Recently, E-corner module, which consists of an IWM, brake-by-wire, steer-by-wire, and active suspension has been proposed as a next-generation modular platform for electric and eco-friendly vehicles [9].…”

Section: Introductionmentioning

confidence: 99%

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Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving

Jeong

Yim

2021

Electronics

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This paper presents an MPC-based integrated control algorithm for an autonomous vehicle equipped with four-wheel independent steering and driving systems. The objective of this research is to improve the performance of the path and velocity tracking controllers by distributing the control effort to the multiple actuators. The proposed algorithm has two modules: reference state decision and MPC-based vehicle motion controller. Reference state decision module determines reference state profiles consisting of yaw rate and velocity in order to overcome the limitation of the error dynamics-based path tracking controller, which requires several assumptions on the reference path. The MPC-based vehicle motion controller is designed with a linear time-varying vehicle model in order to optimally allocate the control effort to each actuator. A linear time-varying MPC is adopted to reduce computational burden caused by using a non-linear one. The effectiveness of the proposed algorithm is validated via simulation on MATLAB/Simulink and CarSim. The simulation results show that the proposed algorithm improves the reference tracking performance by effectively distributing the control effort to the steering angle and driving force of each actuator.

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Section: Reference State Decisionmentioning

confidence: 99%

Section: Reference State Decisionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving

Jeong

Yim

2021

Electronics

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“…We created a control algorithm that predicts inner-wheel spinning during acceleration while turning by calculating the friction limit at which the inner wheel can be driven in real time [3]. A concept is designed for the ELSD clutch to engage based on the difference between the real driving force from the powertrain and the allowable driving force of the inner wheel, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Vehicle Dynamics and Safety through Tire–Road Friction Estimation for Predictive ELSD Control under Various Conditions of General Racing Tracks

Woo,

Jeon,

Joa

et al. 2024

Applied Sciences

Self Cite

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This study focuses on the tire–road friction estimation for the predictive control strategy of electronically limited slip differential (ELSD) to improve the handling and acceleration performance of front-wheel drive cars, which typically suffer from excessive understeer and inner drive wheel spin during acceleration while turning due to reduced vertical load on the wheel. To mitigate this, we propose a control logic for ELSD that enhances course followability and acceleration by pre-transferring the driving torque from the inside to the outside wheel, considering the estimated traction potential for rapid response. It is essential to improve the control accuracy of wheel spin prediction by predicting the friction coefficient of the road surface. Furthermore, this study extends to the analysis of vehicle dynamics during lane-change maneuvers on low-friction surfaces, emphasizing the role of accurate tire–road friction estimation in vehicle safety. A CarSim 2023-based simulation study was conducted to investigate the vehicle response on snowy roads with low friction coefficients (μ = 0.2) and low temperatures (−5 °C). The results demonstrated that even minimal steering input could result in significant side-slip angles, highlighting the nonlinear vehicle behavior and the critical need for robust traction estimation in such challenging conditions of general racing tracks. The proposed friction-estimation method was evaluated through vehicle testing and has been substantiated by patents for its originality in control and friction-estimation approaches. The outcomes of these combined methodologies underline the critical importance of tire–road friction coefficient estimation in both the effectiveness of the ELSD system and the broader context of active safety systems.

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“…However, RWD racing cars are prone to oversteer and loss of control due to rear-wheel slip, especially at high speed and on low-traction surfaces such as wet roads, which greatly reduces the stability of the car [6]. Compared with four-wheel distributed drive (4WD) racing cars, front-wheel dual-motor drive (FWDD) racing cars not only have a simple structure and lower design and maintenance costs but also provide a better driving force for the car by generating additional yaw torque through the differential drive of the two front-wheel motors compared with rear-wheel distributed drive (RWD) racing cars, which improves the maneuverability and stability of the racing car under various track conditions and can be used as a good vehicle for the development of driverless technology [7]. At present, there are very few studies on how to further improve the yaw stability of front-wheel dual-motor drive vehicles in high-speed cornering and driving on low-traction road surfaces, and more in-depth research is needed.…”

Section: Introductionmentioning

confidence: 99%

Research on Yaw Stability Control of Front-Wheel Dual-Motor-Driven Driverless Formula Racing Car

Liu,

Li,

Bai

et al. 2024

WEVJ

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In order to improve the yaw stability of a front-wheel dual-motor-driven driverless vehicle, a yaw stability control strategy is proposed for a front-wheel dual-motor-driven formula student driverless racing car. A hierarchical control structure is adopted to design the upper torque distributor based on the integral sliding mode theory, which establishes a linear two-degree-of-freedom model of the racing car to calculate the expected yaw angular velocity and the expected side slip angle and calculates the additional yaw moments of the two front wheels. The lower layer is the torque distributor, which optimally distributes the additional moments to the motors of the two front wheels based on torque optimization objectives and torque distribution rules. Two typical test conditions were selected to carry out simulation experiments. The results show that the driverless formula racing car can track the expected yaw angular velocity and the expected side slip angle better after adding the yaw stability controller designed in this paper, effectively improving driving stability.

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Active Differential Control for Improved Handling Performance of Front-Wheel-Drive High-Performance Vehicles

Cited by 6 publications

References 18 publications

Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving

Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving

Enhanced Vehicle Dynamics and Safety through Tire–Road Friction Estimation for Predictive ELSD Control under Various Conditions of General Racing Tracks

Research on Yaw Stability Control of Front-Wheel Dual-Motor-Driven Driverless Formula Racing Car

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