Vehicle lift-off modelling and a new rollover detection criterion

Mashadi, Behrooz; Mostaghimi, Hamid

doi:10.1080/00423114.2016.1278076

Cited by 17 publications

(11 citation statements)

References 17 publications

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“…Furthermore, most of the tire models are dependent on the vehicle vertical force acting ( N z ) on each tire. The vertical force is often calculated using the load transfer equation [10]. The load transfer equation requires the mass of the vehicle to estimate the vertical force.…”

Section: Introductionmentioning

confidence: 99%

Effect of load distribution on longitudinal and lateral forces acting on each wheel of a compact electric vehicle

et al. 2020

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Autonomous vehicle is gaining popularity in the market worldwide. Most autonomous vehicle are based on electric vehicles since they are easy to control. The torque control of electric vehicles is precise and easy since electric motor torque can be manipulated by controlling the motor current. Furthermore, the load of the vehicle affects the motor torque of an electric vehicle. A higher vehicle load requires high motor torque to propel the vehicle. Often in autonomous vehicle, the vehicle parameters and stability measure are set within a limit based on standard vehicle settings. However, a vehicle loaded with extra mass on either side can offset these parameters affecting the efficiency of the controller. Thus, in this paper the effect of load on vehicle longitudinal and lateral forces is identified. A simulation model of a two rear in-wheel motored electric vehicle is developed. The model is used to analyze the effect of load on vehicle longitudinal and lateral forces. Based on the result, increasing the load on the side of direction of lateral motion increases the lateral force generated. The high lateral force causes the tires to approach the tire friction circle limit. This can affect the automated vehicle performance since the tires are in unstable region.

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Section: Introductionmentioning

confidence: 99%

Effect of load distribution on longitudinal and lateral forces acting on each wheel of a compact electric vehicle

et al. 2020

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“…In addition, passive vehicle rollover protection systems, such as rollover warning systems, are often used for rollover control [14]- [19]. For example, Mashadi et al [20] proposed the vehicle dynamics model when the wheels were lifted, and deduced the rollover threshold. Li et al [21] designed an improved predictive LTR (IPLTR) to predict load transfer rate as a rollover warning index.…”

Section: Introductionmentioning

confidence: 99%

Research on a Novel Vehicle Rollover Risk Warning Algorithm Based on Support Vector Machine Model

Zhu

Yin

et al. 2020

IEEE Access

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A novel vehicle rollover warning algorithm based on support vector machine (SVM) empirical model is proposed to improve the real-time of un-tripped rollover warning algorithm and accuracy of dynamic rollover warning. Considering the nonlinear characteristic of driver-vehicle-road interaction and the uncertainty of modeling, the traditional deterministic methods cannot meet the requirements of accurate vehicle rollover warning modeling. The probability method considering issues of uncertainty is applied to design vehicle dynamic rollover warning algorithm. The SVM empirical model considers the uncertainties of the driver-vehicle-road system and the real variability of the parameters, provides an explicit function of vehicle rollover safety limit and its gradient, and utilizes the hypersurface visualization boundary to define the rollover safety area and the unsafe area. Targeting on sport utility vehicle under the condition of high-speed emergency obstacle avoidance, simulations are carried out to verify the proposed vehicle rollover warning algorithm based on SVM empirical model and of the simulation results show that the proposed algorithm has accurate warning and good real-time performance. It can effectively improve the warning accuracy of vehicle dynamic rollover, reduce the interference of nonlinear and uncertainty, and significantly improve the active safety performance with vehicle rollover prevention. INDEX TERMS rollover warning algorithm, support vector machine, empirical model TIANJUN ZHU was born in Xingtai City, Hebei province, China in 1977. He received the B.S. degree in vehicle engineering from Hebei Agricultural University, Baoding, in 2000. and M.S. degree in mechanical engineering from

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“…Li et al [12] designed an improved predicted load transfer ratio IPLTR as rollover warning index. Mashadi and Mostaghimi [13] derived vehicle dynamics models after the wheels lift-off and developed the rollover threshold based on the governing equation. The best time for the prevention of the vehicle rollover is obtained by applying a correcting moment.…”

Section: Introductionmentioning

confidence: 99%

A Reliability Approach to Development of Rollover Prediction for Heavy Vehicles Based on SVM Empirical Model With Multiple Observed Variables

Zhu

Yin

et al. 2020

IEEE Access

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The rapid development of cooperative vehicle-infrastructure system (CVIS) improves the communication reliability between vehicles and road environment. These communications enable the accurate vehicle rollover prediction in Human-Vehicle-Road interaction. However, considering the strong nonlinear characteristics of Human-Vehicle-Road interaction and the uncertainty of modeling, the traditional deterministic method cannot meet the requirement of accurate prediction of rollover hazard for heavy vehicles. In order to improve the accuracy of vehicles rollover prediction, this paper proposes a developed rollover prediction algorithm based on the multiple observed variables by combining the failure probability in reliability and the empirical model. This approach applies the probability method of uncertainty to the design of dynamic rollover prediction algorithm for heavy vehicles and establishes a classification model of heavy vehicles based on support vector machine (SVM) with multiple observed variables. The failure probability of rollover limit state of heavy vehicles is calculated by Monte Carlo Sampling (MCS), Radial-Based Importance Sampling (RBIS), and Truncated Importance Sampling (TIS), respectively. Then the Fishhook, Double Lane Change tests, and J-turn tests, simulated in TruckSim, are carried out to validate the proposed algorithm. The simulation results show that the rollover prediction algorithm based on failure probability can effectively improve the rollover prediction accuracy for heavy vehicles. Moreover, based on the communication in CVIS, the failure probability can be obtained before entering the specific road. Meanwhile, this approach can reduce the external interference of strong non-linear characteristics of Human-Vehicle-Road interaction and the uncertainty of the modeling to the system, thus improving the prediction accuracy of active safety performance of heavy vehicles significantly. INDEX TERMS Failure probability, heavy vehicles, load transfer ratio, rollover risk prediction, SVM classification model. BIN LI received the Ph.D. degree from Shanghai Jiao Tong University, China, in 2010. He was Research Fellow on electric vehicle project with the University of Waterloo and on mobile robotic control project with McGill University. He is currently a Researcher working on stability and safety control of commercial vehicle and passenger car with Concordia University. His research interests include vehicle system modeling, dynamics and control, vehicle control system design and optimization, electrified vehicle, integrated vehicle motion control, and autonomous vehicle control. WEI MA was born in Xingtai, Hebei, China, in 1991. He received the B.S. degree in vehicle service engineering from the Hebei University of Engineering, Handan, China, in 2018, where he is currently pursuing the M.S. degree in equipment intelligence and safety engineering. His research interest includes vehicle dynamic control.

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Vehicle lift-off modelling and a new rollover detection criterion

Cited by 17 publications

References 17 publications

Effect of load distribution on longitudinal and lateral forces acting on each wheel of a compact electric vehicle

Effect of load distribution on longitudinal and lateral forces acting on each wheel of a compact electric vehicle

Research on a Novel Vehicle Rollover Risk Warning Algorithm Based on Support Vector Machine Model

A Reliability Approach to Development of Rollover Prediction for Heavy Vehicles Based on SVM Empirical Model With Multiple Observed Variables

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