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

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

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 2 CONFIDENTIAL

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 Braking Development SCS Functions Stability Attribute Systems Applications Virtual Tools SCS Analysis 3 CONFIDENTIAL

Introduction SCS Robustness Testing • The SCS calibration development process Static & dynamic vehicle property • High Mu calibration • Medium Mu • Low Mu • Off road (Land Rovers) Braking, traction, • Calibration robustness & threshold consumption yaw, & roll • Tests to check for pump duty cycle & false stability tuning interventions – typically public road routes • Roll Stability Control function = Covara, Italy Robustness & Simulation Models: Validation testing Vehicle Controller Road Driver 4 CONFIDENTIAL

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. 5 CONFIDENTIAL

Modelling Strategy Vehicle Model: How Confidence is Obtained 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. 6 CONFIDENTIAL

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 7 CONFIDENTIAL

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. 8 CONFIDENTIAL

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. 9 CONFIDENTIAL

Modelling Strategy Analysis Methods 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 GG diagram (Density Plot) 10 CONFIDENTIAL

Results Comparison of IPG Driver to Experimental Data Experimental Data Defensive Lateral Acceleration [ms-2 ] Lateral Acceleration [ms-2 ] Longitudinal Acceleration [ms-2 ] Longitudinal Acceleration [ms-2 ] Normal Aggressive Lateral Acceleration [ms-2 ] Lateral Acceleration [ms-2 ] 11 CONFIDENTIAL Longitudinal Acceleration [ms-2 ] Longitudinal Acceleration [ms-2 ]

Results Sensitivity of IPG Rally Driver to Parameters Side Slip Brake Slip Coeff 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 Lateral Acceleration [ms -2 ] Lateral Acceleration [ms -2 ] Long Acceleration [ms -2 ] Long Acceleration [ms -2 ] 12 CONFIDENTIAL

Analysis Modifications to the Standard Driver 13 CONFIDENTIAL

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 14 CONFIDENTIAL

Analysis Current State of the Driver Model Correlation G-G Diagram Correlation. Lateral Acceleration [g ] Longitudinal Acceleration ms-2] Lateral Acceleration [g ] Longitudinal Acceleration [g] 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 15 CONFIDENTIAL

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 16 CONFIDENTIAL

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! 17 CONFIDENTIAL

Alexander Shawyer. SCS Analysis, Vehicle Characterisation. ashawyer@jaguarlandrover.com Alex Bean. Technical Specialist, SCS Analysis. & Virtual Tools abean@jaguarlandrover.com