Torque-Vectoring Control for Fully

Electric Vehicles: Model-Based Design,

Simulation and Vehicle Testing

Leonardo De Novellis, Aldo Sorniotti, Patrick Gruber

University of Surrey, UK

a.sorniotti@surrey.ac.uk

1st September 2011 – 31st August 2014

IPG Apply and Innovate Conference

Karlsruhe, Germany, 24th September 2014

Outline

1. E-VECTOORC consortium;

2. Objectives;

3. Simulation models;

4. Control structure;

5. Simulation-based testing;

6. Experimental testing;

7. Conclusions

10 highly committed partners, with complementary skills and expertise:

3 large industrial companies (Jaguar Land Rover, SKODA Auto and TRW), 2 SMEs (Inverto and ViF), 3

research centres (CIDAUT, ITA and Flanders’ Drive), 2 universities (TUIL and Surrey);

6 countries involved (Austria, Belgium, Czech Republic, Germany, Spain, United Kingdom)

1. E-VECTOORC Consortium

Range Rover Evoque electric vehicle demonstrator

• Electro-hydraulic braking system unit with newly

developed control software

• Four on-board switched reluctance electric drivetrains;

Inverto electric motors

Drivetrain assemblies

TRW SCB unit

1. E-VECTOORC Consortium

Steering wheel angle

Lateral acceleration

Linear region

Non-linear region

Asymptote

0.4-0.5 g

Possible effects of torque-vectoring

V=const

Design of the reference understeer characteristic through torque-vectoring control

2. Objectives

1. Constant torque distribution 2. Torque-proportional-to-Fz torque-vectoring

Strategy 2. allows smaller range of variation of the understeer gradient but does not

allow the design of the vehicle understeer characteristic

2. Objectives

Compensation of the variation of the understeer characteristic as a function of

longitudinal acceleration and deceleration

Torque-vectoring distribution with torque-proportional-to-vertical-load (Aisin – US

Patent No. 5148883, Shimada-Shibahata SAE paper 940870)

3. Simulation models Quasi-static model

• Not requiring the forward integration of the equations of motion in

the time domain;

• Time derivatives of the state variables (e.g. yaw rate, roll angle and

slip ratios) equal to zero;

• Ideal for evaluating the understeer characteristic in conditions of

non-zero longitudinal acceleration

IPG CarMaker – Simulink model

• Drivetrain dynamics modelled in Matlab-Simulink;

• Vehicle chassis model in CarMaker;

• Model for control system performance assessment and fine-tuning

3. Simulation models

The yaw moment controller presents a hierarchical structure

4. Control Structure

*

,

*

,

*

if

if

*0,

*

dyndynMAXydynMAXyy

dyndyndyny

dyndyn

dyndyn

eakaa

ka

understeer gradient: Ku = k-1

linear region threshold: ay* = kdyn

*

asymptotic value: ay,MAX = kdyn,0

Sport mode (smaller KU, increased ay*, ay,MAX)

Normal mode (≈baseline vehicle)

Eco mode (same as normal mode)

The exponential approximation fits well with the experimental results and can

be used for the analytical definition of the reference vehicle behaviour

4. Control Structure

An optimisation procedure has been developed for achieving the target

understeer characteristic through the feedforward contribution of the yaw

moment

Several objective functions have been implemented

4. Control Structure

1. Quasi-static model and offline optimisation procedure are employed considering

an objective function (e.g., the minimisation of the overall input motor power)

2. The look-up tables of MzFF as function of , ax, V, m are implemented in the

controller

3. The off-line optimisation

procedure can be used for

sensitivity analyses, e.g., the

evaluation of the impact of the

understeer gradient on power

consumption

The procedure works!

4. Control Structure

Driving mode Understeer characteristic Control Allocation

Normal Normal Squared sum of wheel torque-vertical load ratios

Sport Sport Squared sum of wheel torque-vertical load ratios

Eco Normal Wheel torque ratio from offline optimisation

On-line optimisation methods have been chosen for the 3 driving modes

with differences in the cost function formulation

1) Tyre friction limits

2) Motor/regeneration torque limits

3) Maximum predictive torques

4) Battery (charge/discharge) limits

5) ECE Braking regulations

6) Rate limits

Main constraints for CA

4. Control Structure

PID + Feedforward

Second order sub-optimal sliding mode

Twisting second order sliding mode

Integral sliding mode

H-infinity based on loop-shaping

Sim. Exp. SS

x x x

x x

x

x x

x x x

Sim.: assessed through IPG CarMaker simulations

Exp.: assessed through experiments

SS: including sideslip control formulation

4. Control Structure

Sub-optimal SOSM

Baseline PID+FF

(V = 90 km/h; pa = 50 %)

De Novellis, Sorniotti, Gruber, Pennycott, “Comparison of Feedback Control Techniques for Torque-Vectoring Control of Fully Electric Vehicles”, IEEE TVT (2014)

5. Simulation-based testing

Lommel proving ground

Skid pad tests (e.g., R = 30, 60 m)

Step steer tests at constant torque demand (e.g., = 100 deg, Vin = 90 km/h)

Frequency response tests at constant torque demand ( = 20 deg, Vin = 50, 90 km/h)

Vehicle modes: baseline, torque-vectoring (sport mode, normal mode, VSC mode)

6. Experimental testing

• Three driving modes (sport, normal, eco) selectable by the driver;

Torque-vectoring controller

• Vehicle response ‘designed’ through the controller

Skid pad test results

6. Experimental testing

Skid pad test

results

Step steer results

• Increased yaw damping;

• Reduced delay

6. Experimental testing

Time delay (in s) between the reference yaw rate and the actual yaw rate

7. Conclusions

1. Model-based design of the feedforward contribution of the torque-

vectoring controller;

2. Control structure including feedforward and feedback contributions,

designed for reduced amount of tuning time;

3. Experimental demonstration of the capability of shaping the understeer

characteristic depending on the selected driving mode;

4. Experimental demonstration of the benefits (in terms of increased yaw

damping) of continuous torque-vectoring control actuated through the

electric motor drives with respect to the actuation of the friction brakes in

emergency conditions

Happy to answer questions

www.e-vectoorc.eu

The research leading to these results

has received funding from the European

Union Seventh Framework Programme

FP7/2007-2013 under grant agreement

n°284708

For information do not hesitate to contact Aldo Sorniotti, a.sorniotti@surrey.ac.uk