[PPT] - Modification of IPG Driver for Road Robustness Applications PowerPoint Presentation

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Modification of IPG Driver for Road Robustness Applications

Alexander Shawyer (BEng, MSc) Alex Bean (BEng, CEng. IMechE) SCS Analysis & Virtual Tools, Braking Development Jaguar Land Rover

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Introduction

Presentation Contents

1. Introduction
Virtual Engineering at JLR
Stability Controls Development
2. Modelling Strategy
Hypothesis
Vehicle Model
Road Model
Analysis Methods
3. Results
Standard Driver
Rally Driver
Parameterisation
4. Conclusions & Further Work

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Introduction

Virtual Engineering at JLR

JLR is investing significant effort & resource into developing virtual engineering capability across Product Development

Efficiency – facilitates earlier engineering & decisions
Robustness – increased design space & test scenario evaluation
Cost - reduced prototype fleet & physical testing

Stability & ABS functions development have traditionally been very vehicle intensive activities, involving significant overseas tests trips. Chassis Engineering Brakes Design SCS Functions Systems Braking Development Stability Attribute Applications Virtual Tools SCS Analysis

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Introduction

SCS Robustness Testing

The SCS calibration development process

Static & dynamic vehicle property calibration Braking, traction, yaw, & roll stability tuning Robustness & Validation testing

High Mu
Medium Mu
Low Mu
Off road (Land Rovers)
Calibration robustness & threshold consumption
Tests to check for pump duty cycle & false

interventions – typically public road routes

Roll Stability Control function = Covara, Italy

Simulation Models: Vehicle Controller Road Driver

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Modelling Strategy

Hypothesis & Method

Hypothesis The IPG Driver model with suitable parameterisation could be applicable to various SCS applications. More specifically; correlating the IPG driver to real driver data would enable virtual Road Robustness testing. Focusing on the general Driver behaviour such as G-G Diagrams, time intensities and peak accelerations we can optimise the Driver model to better represent real driver performance. Sensitivity Analysis A sensitivity analysis is performed to identify the key driver parameters that have the greatest influence on the driver performance. Standard Driver Each Parameter is swept through a range of +/-15% at intervals of 1%. Rally Driver Side Slip & Brake Slip Coefficient: 0.5 – 6.0 @ 1.0 intervals.

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Correlation (Static) K & C correlation.

Bump Steer, Roll Centre Height, Track Change & Wheelbase Change.

Correlation (Dynamic).

Constant Radius.
Understeer Gradient.
Frequency Response.
Roll, Pitch & Yaw Frequency

Other Tyres scaled correctly for Asphalt:

LKY: Scaling factor for cornering stiffness.
LMUY: Scaling factor for peak lateral friction.

Modelling Strategy

Vehicle Model: How Confidence is Obtained

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Modelling Strategy

Static Correlation: Vehicle Model

Exp CarMaker Front Mass [kg] 1197 1196 Rear Mass [kg] 799 800 Total Mass [kg] 1996 1996 Average steering ratio 14.8- Wheelbase [m] 2.660 2.662 Tyres pressure Exp CarMaker Front [bar] 2.48 2.48 Rear [bar] 2.20 2.20 Toe Exp CarMaker Front left [deg] 0.12 0.13 Front right [deg] 0.12 0.13 Rear left [deg] 0.07

0.01

rear right [deg] 0.07

0.01

Camber Exp CarMaker Front left [deg]

0.48
0.41

Front right [deg]

0.48
0.41

Rear left [deg]

1.16
1.13

rear right [deg]

1.16
1.13

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Modelling Strategy

Road Model Generation

Using a JLR in-house ‘Road Builder’ tool; GPS data from the Covara test route was processed and converted into a .road file.

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Modelling Strategy

Segment Selection

This segment has been selected for this analysis because it offers a sufficient mix of corners to generate the necessary Acceleration range to effectively optimise the Driver Model.

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Modelling Strategy

Analysis Methods

GG diagram (Density Plot) Time Intensity To assess improvements in the driver model, the following plots will be used. These plots are designed to show the Driver Character. The primary concern is developing a driver model that can be utilised in a wider context as opposed to a specific use case. Time History

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Results

Comparison of IPG Driver to Experimental Data

Defensive Normal Aggressive Experimental Data

Lateral Acceleration [ms-2] Longitudinal Acceleration [ms-2] Lateral Acceleration [ms-2] Longitudinal Acceleration [ms-2] Lateral Acceleration [ms-2] Longitudinal Acceleration [ms-2] Lateral Acceleration [ms-2] Longitudinal Acceleration [ms-2]

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Results

Sensitivity of IPG Rally Driver to Parameters

Side Slip Coeff Brake Slip Coeff Ax [ms-2] Ay [ms-2] Car V [kph] Yaw Rate [degs-1] Time [s] Distance [m] Side Slip Angle [deg] 0.5 0.5

5.943
4.874

130.13

24.102

59.929 571.904

1.7241

6 6

5.944
4.874

130.18

24.102

59.929 571.904

1.724

The initial testing of the Rally driver model showed little difference over the standard Driver model. Reasons; Rally Driver not intended for this scenario (4WD vehicle model and higher friction surface). SS Coeff = 0.5, BS Coeff = 0.5 SS Coeff = 6.0, BS Coeff = 6.0

Long Acceleration [ms-2] Lateral Acceleration [ms-2] Lateral Acceleration [ms-2] Long Acceleration [ms-2]

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Analysis

Modifications to the Standard Driver

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Analysis

Fine tuning of the Additional Driver Parameters

Utilising all the available driver parameters resulted in and unstable behaviour of the driver model. To improve stability of the model just two parameters are chosen to fine tune the driver model: Long.AccuarcyCoef = 0.95 Long.SmootCoef = 0.75

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Analysis

Current State of the Driver Model Correlation

Time [s] Distance [m] Min Long Acc [ms-2] Max Long Acc [ms-2] Min Long Acc [ms-2] Max Long Acc [ms-2] 67.31 595.2058

3.4335

2.6487

8.9271

7.848 43.5 478.715

3.1261

2.421

6.2787

7.1264

G-G Diagram Correlation.

Longitudinal Acceleration ms-2] Lateral Acceleration [g] Longitudinal Acceleration [g] Lateral Acceleration [g]

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Conclusions

Standard driver Aggressive model best represented real test driver data

but significant parameter tuning required to obtain best correlation:-

Max Longitudinal & Lateral Acceleration, Min Long Acceleration &

Corner Cutting Coefficient.

Apex Shift Coefficient, Corner Roundness Coefficient , Throttle

Accuracy & Smoothness

Rally Driver not effective for this Road Robustness scenario
Designed for steady state drift scenario
Actual usecase was 4WD high mu surface (i.e. no high slip angles)
Parameterisation of driver model to produce a similar G-G plot is the

biggest challenge

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Further work

Working with IPG to develop standard procedure for parameterising driver

model to experimental data

Further evaluate Rally Driver for low mu test scenarios
Improve road measurement process for road model creation
Consider use of steering torque as a feedback loop to improve

representation of real driver Ultimate Goal A set of driver models to represent typical US, European & Chinese drivers …& SCS Development Engineers!

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Alexander Shawyer.

SCS Analysis, Vehicle Characterisation. ashawyer@jaguarlandrover.com

Alex Bean.

Technical Specialist, SCS Analysis. & Virtual Tools abean@jaguarlandrover.com