Design of a central feedforward control of torque vectoring and rear-wheel steering to beneficially use tyre information

This study investigates the limited-slip and steering characteristics of a dual continuously variable transmission system. The dual continuously variable transmission is a unique final drive system composed of two continuously variable transmissions, with one continuously variable transmission connected to each rear wheel. In this study, a dynamic model of the dual continuously variable transmission system is derived, and models of the conventional final drive systems, i.e. the solid axle and the open differential, are used as benchmarks. In the simulations, the dual continuously variable transmission model, the solid axle model and the open differential model are applied to a vehicle dynamic model for split- μ road tests and a series of steering tests. According to the results of the split- μ road tests, the limited-slip function of a dual continuously variable transmission system is verified. The results of the steering tests show that different torque distributions for the inside wheels and the outside wheels while cornering can be controlled with different gain values of the continuously variable transmissions; for this reason, the application of the dual continuously variable transmission system as a torque-vectoring device is proposed, and a basic setting principle is presented. The results of this study establish a fundamental knowledge for developing the dual continuously variable transmission as an advanced final system for improving the vehicle dynamics.

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Inverse Model Based Torque Vectoring Control For a Rear Wheel Driven Battery Electric Vehicle

IFAC Proceedings Volumes ◽

10.3182/20140824-6-za-1003.00802 ◽

2014 ◽

Vol 47 (3) ◽

pp. 12016-12022 ◽

Cited By ~ 7

Author(s):

T. Bünte ◽

S. Kaspar ◽

S. Hohmann ◽

J. Brembeck

Keyword(s):

Electric Vehicle ◽

Rear Wheel ◽

Inverse Model ◽

Battery Electric Vehicle ◽

Model Based ◽

Torque Vectoring

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Predictive rear wheel torque vectoring control with terminal understeer mitigation using nonlinear estimation

2015 54th IEEE Conference on Decision and Control (CDC) ◽

10.1109/cdc.2015.7402890 ◽

2015 ◽

Cited By ~ 3

Author(s):

Efstathios Siampis ◽

Efstathios Velenis ◽

Stefano Longo

Keyword(s):

Nonlinear Estimation ◽

Rear Wheel ◽

Torque Vectoring ◽

Wheel Torque

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Design of rear wheel torque vectoring for dual motor Electric drive system

2014 Ninth International Conference on Ecological Vehicles and Renewable Energies (EVER) ◽

10.1109/ever.2014.6844114 ◽

2014 ◽

Cited By ~ 3

Author(s):

Balasharmila Rao Munusamy ◽

Joerg Dieter Weigl

Keyword(s):

Electric Drive ◽

Rear Wheel ◽

Drive System ◽

Electric Drive System ◽

Torque Vectoring ◽

Wheel Torque

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Torque Vectoring Control of RWID Electric Vehicle for Reducing Driving-Wheel Slippage Energy Dissipation in Cornering

Energies ◽

10.3390/en14238143 ◽

2021 ◽

Vol 14 (23) ◽

pp. 8143

Author(s):

Junnian Wang ◽

Siwen Lv ◽

Nana Sun ◽

Shoulin Gao ◽

Wen Sun ◽

...

Keyword(s):

Energy Dissipation ◽

Electric Vehicle ◽

Control Strategy ◽

Rear Wheel ◽

Recursive Least Squares ◽

Wheel Slip ◽

Saturation Region ◽

Torque Vectoring ◽

Driving Wheel ◽

Driving Range

The anxiety of driving range and inconvenience of battery recharging has placed high requirements on the energy efficiency of electric vehicles. To reduce driving-wheel slip energy consumption while cornering, a torque vectoring control strategy for a rear-wheel independent-drive (RWID) electric vehicle is proposed. First, the longitudinal linear stiffness of each driving wheel is estimated by using the approach of recursive least squares. Then, an initial differential torque is calculated for reducing their overall tire slippage energy dissipation. However, before the differential torque is applied to the two side of driving wheels, an acceleration slip regulation (ASR) is introduced into the overall control strategy to avoid entering into the tire adhesion saturation region resulting in excessive slip. Finally, the simulations of typical manoeuvring conditions are performed to verify the veracity of the estimated tire longitudinal linear stiffness and effectiveness of the torque vectoring control strategy. As a result, the proposed torque vectoring control leads to the largest reduction of around 17% slip power consumption for the situations carried out above.

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Improvement of Vehicle Stability Using Reinforcement Learning

10.5753/eniac.2018.4420 ◽

2018 ◽

Author(s):

Janaína R. Amaral ◽

Harald Göllinger ◽

Thiago A. Fiorentin

Keyword(s):

Reinforcement Learning ◽

Learning Algorithm ◽

Rear Wheel ◽

Vehicle Stability ◽

Sideslip Angle ◽

Vehicle Handling ◽

Race Car ◽

Torque Vectoring ◽

Preliminary Study ◽

The Cost

This paper presents a preliminary study on the use of reinforcement learning to control the torque vectoring of a small rear wheel driven electric race car in order to improve vehicle handling and vehicle stability. The reinforcement learning algorithm used is Neural Fitted Q Iteration and the sampling of experiences is based on simulations of the vehicle behavior using the software CarMaker. The cost function is based on the position of the states on the phase-plane of sideslip angle and sideslip angular velocity. The resulting controller is able to improve the vehicle handling and stability with a significant reduction in vehicle sideslip angle.

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