Torque-Vectoring Control for Fully Electric Vehicles: Model-Based - - PowerPoint PPT Presentation

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

* , *

dyn dyn MAX y dyn MAX y y dyn dyn dyn y

dyn dyn dyn dyn

e a k a a k a      

   

• understeer gradient: Ku = k-1
• linear region threshold: ay

* = kdyn *

• asymptotic value: ay,MAX = kdyn,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 Mz

FF 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