Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles

Siampis, Efstathios; Velenis, Efstathios; Longo, Stefano

doi:10.1080/00423114.2015.1064972

Cited by 51 publications

(33 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a minimum-time cornering problem, Berntorp et al [20] showed the influence of the tire and chassis models on the computational complexity and performance of an NMPC, without providing a general conclusion. Siampis et al [21] demonstrated that the wheel dynamics do not bring a significant performance enhancement and increase complexity.…”

Section: Introductionmentioning

confidence: 99%

On Prediction Model Fidelity in Explicit Nonlinear Model Predictive Vehicle Stability Control

Metzler¹,

Tavernini

Gruber

et al. 2021

IEEE Trans. Contr. Syst. Technol.

View full text Add to dashboard Cite

This study discusses vehicle stability control based on explicit nonlinear model predictive control (NMPC) and investigates the influence of prediction model fidelity on controller performance. The explicit solutions are generated through an algorithm using multiparametric quadratic programming (mp-QP) approximations of the multiparametric nonlinear programming (mp-NLP) problems. Controllers with different prediction models are assessed through objective indicators in sine-with-dwell tests. The analysis considers the following prediction model features: 1) nonlinear lateral tire forces as functions of slip angles, which are essential for the operation of the stability controller at the limit of handling. Moreover, a simple nonlinear tire force model with saturation is shown to be an effective alternative to a more complex model based on a simplified version of the Magic Formula; 2) longitudinal and lateral load transfers, playing a crucial role in the accurate prediction of the lateral tire forces and their yaw moment contributions; 3) coupling between longitudinal and lateral tire forces, which has a significant influence on the front-to-rear distribution of the braking forces generated by the controller; and 4) nonlinear peak and stiffness factors of the tire model, with visible yet negligible effects on the results.

show abstract

Section: Introductionmentioning

confidence: 99%

On Prediction Model Fidelity in Explicit Nonlinear Model Predictive Vehicle Stability Control

Metzler¹,

Tavernini

Gruber

et al. 2021

IEEE Trans. Contr. Syst. Technol.

View full text Add to dashboard Cite

show abstract

“…But if the target torque is constrained by the motor limit, the desired velocity and yaw motion requested by the driver may not be satisfied. By considering the longitudinal slip and motor torque constraint, MPC was utilized for the rear wheel torque vectoring, 19 but this study is based on the assumption that front-rear brake distribution is fixed.…”

Section: Introductionmentioning

confidence: 99%

Torque vectoring system design for hybrid electric–all wheel drive vehicle

Cho

Huh

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

A torque vectoring system is designed for the hybrid electric–all wheel drive vehicle where the front and rear wheels are powered by the combustion engine and electric motors, respectively. The vehicle provides enhanced handling performance by a twin motor drive unit that can distribute the driving and regenerative braking torques to the rear-left and rear-right wheels independently. Based on the driver’s intention, a sliding mode controller is designed to calculate the desired traction force and yaw moment for the vehicle. The force distribution between the front and rear axles is investigated considering the principle of the friction circle, and characteristics of the engine and drive motors. The vertical tire force is estimated using the random walk Kalman filter for the proportional distribution between the front and rear longitudinal forces. For the torque distribution between the rear-left and rear-right wheels, an optimization problem is formulated by considering the constraints of the friction circle and motor characteristics. The proposed algorithm is evaluated in a simulation environment first by reflecting the characteristics of the hybrid electric–all wheel drive modules. Then, the test vehicle is utilized to validate the handling performance experimentally and to compare with the uncontrolled cases.

show abstract

“…Fuzzy logic control (FLC) was used in [16,17], introducing a set of logic rules that formulate the control law and thus avoiding the complexity of dealing with nonlinear model. Successive linearization was used by [18] to linearize the model around current state and by [19] to linearize the model around current equilibrium point, both applying model predictive control (MPC) methods. A very popular approach for dealing with nonlinearity is gain-scheduling.…”

Section: Introductionmentioning

confidence: 99%

Yaw stability control for a rear double-driven electric vehicle using LPV-H∞ methods

Weiss

Allerhand²,

Arogeti

2018

Sci. China Inf. Sci.

View full text Add to dashboard Cite

This paper presents a new yaw stability controller for a rear double-driven electric vehicle. A linear parameter varying (LPV) model of the vehicle is formulated using longitudinal speed measurement and tire cornering stiffness estimation. The LPV model is then utilized to design a gain-scheduled H∞ controller with guaranteed stability. Results from simulations, performed with CarSim, show that the new controller improves the vehicle performance and handling even in extreme maneuvers and that it is robust to model parameter uncertainties.

show abstract

Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles

Cited by 51 publications

References 20 publications

On Prediction Model Fidelity in Explicit Nonlinear Model Predictive Vehicle Stability Control

On Prediction Model Fidelity in Explicit Nonlinear Model Predictive Vehicle Stability Control

Torque vectoring system design for hybrid electric–all wheel drive vehicle

Yaw stability control for a rear double-driven electric vehicle using LPV-H∞ methods

Contact Info

Product

Resources

About