Speakers Information- Controls, Measurement & Calibration Congress
‘Global Modelling Technique’ to Model Multiple Engine Variants for M&HCV BSIII Application Anuroopa Varsha Tata Motors Ltd.
ABSTRACT This paper describes the application of Global Modelling Technique to calibrate six cylinder six litre medium duty common rail commercial engine with best fuel economy for BS III/Euro III application for four engine sub variants. Global Modelling Technique has been applied to map the entire engine operating range, which avoids the need to model the sub-variants independently. These sub variants are classified based on peak power and torque ratings. Good correlation between predicted and measured data was achieved for the chosen modelled responses and about 8% improvement in fuel efficiency was achieved over existing traditionally calibrated variant1.
1. INTRODUCTION Indian market traditionally has always been very sensitive to fuel efficiency. The introduction of strict emission norms has thrown up lot of challenges not only to meet them, but also to have better fuel efficiencies. Because of the higher commercials involved in the modern day electronic engines, higher stress has been put on to deliver better fuel efficiency than the existing solutions (rotary fuel pump or mechanical systems) to offset the increased cost. The increased commercials also demand commonization of the engine hardware for all the derived sub ratings of the engine. With the advancement of new technologies, the number of variables to be calibrated has also increased fourfold. Because of these complexities good calibration tools and methodologies are necessary. In this work, the AVL CAMEO Global Modelling Technique was suitably applied to already existing two variants and two new variants which were deemed necessary. The engine is six cylinders six litre with medium duty common rail, high pressure line cooled EGR and waste gate turbocharger. The whole operating range of the engine variants were mapped with this technique with special considerations given to the vehicle operating regions. The Global Modelling Technique was found to be very effective tool to model and calibrate multiple engine sub rating derived for the same engine. The predictions of the model were found to be very accurate.
2.
OBJECTIVE
The following were the objectives.
Improve existing engine calibration of two variants for minimum fuel consumption based on new road load data and to further calibrate two new engine sub variants.
Include and optimize nodal points derived from actual road load cycle.
Complete the calibration process in minimum time and minimum utilization of test bed resources.
3. NEED FOR GLOBAL MODEL Traditional methods such as One Factor At a Time (OFAT) and factorial methods are not suitable for modern day electronic engines as the entire variation space and any interaction effect are not covered in OFAT and the later would require practically impossible large number of operating points. In DOE local modelling technique, the user needs to specify the operating points in terms of engine speed and torque (in this case 13 mode ESC points) and then a variation space (ECU variables) is constructed for each operating point. This method with 4th order D-Optimal test and with 5 ECU variables as variation space would require a minimum of 75 points (including additional and repetition points). For this case the total number of variation points would be 3675 (4X12X75 for 12 points of ESC + 75 points for one common idle point). With these many number of points only ESC regions of sub1
One of the variant was calibrated using One Factor at A Time approach by Tier 1 supplier and the other using the Local Modelling Technique of Cameo.
rating of the engine is modelled and any additional point (such as derived from vehicle operation) would require further addition of points. Global Modelling Technique provides alternative and effective methodology to model entire engine operating region and is the most suitable method for modelling multiple sub rating engines. Global Model Technique is a process in which engine speed and torque/injected quantity is also made as a variation in the test design. The advantages include the ability to predict engine response as a function of variation parameters throughout the modelled region, calibrate for transients and make different calibrations of the engine depending on the intended application. In this work about 3500 points were used to model the entire engine operating region for 4 sub-variants.
4. CALIBRATION PROCESS Please refer to Figure 4.1 which shows the workflow used for the global modelling process. As stated earlier, base engine calibration was already available for two of the variants. With this base calibration, part load data for the engine was generated. These points were used as the start/safe point for the operating points in the global model.
Part load data generation throughout the engine operating region with the existing calibration. Division of the engine operating region into 4 subregions based on emission and non-emission region and ECU variations chosen. Screening test to determine the boundary points based on the limit imposed on specific NOx in g/kW.h
Global model preparation based on the operating regions
Test run of modelled regions and test data collection
Global modelling and optimization
Map generation
Verification of the predicted optimized results
Figure 4.1: Workflow for Calibration Process using Global Modelling Technique Since the emission region in M&HCV application extends from ‘A’ speed to ‘C’ speed with torque level ranging from 100% to 25%, engine operating regions were divided into two parts based on emission and non-emission regions. The other two parts came from the ECU variation parameters. Thus the whole operating region was mapped using four separate regions. This is shown in Figure 4.2. Idling was modelled as a local point to complete the model.
Figure 4.2: Global Model Regions
The maximum and minimum operating envelope as function of engine speed and torque were created by running a screening test with a maximum and minimum limit imposed on specific NOx (in g/kWh) for engine speed at every 100 rpm and engine torque varying from 100% to 20% of maximum torque with a step of 20% of maximum torque by varying ECU parameters. Global Model was then prepared for the four regions with a total of about 3500 points for the four regions. After completing the test run, model was prepared and validated.
5. GLOBAL MODEL PROCESS 5.1. TEST RUN AND MEASUREMENT The Global Model test was parameterized with the following variation parameters i.
Engine Speed
ii.
Engine Brake Torque
iii.
Rail Pressure
iv.
Injection Timing
v.
EGR valve position
vi.
Pilot Quantity
vii.
Pilot Separation
In order to generate a good model, the fidelity of the collected measurement data should be very high. In order to achieve this, ‘Individual Channel Stabilization’ feature of CAMEO was used to stabilize the engine at every variation point by monitoring and stabilizing the selected channels. The drifts in measurement data of various measuring instruments was also checked at every repetition point. The test cycle was stopped if any drift in the measurement instrument was found. The measurement instrument was corrected/re-calibrated and test run was continued after this process. 5.2. MODELLING Special attention was given to the following responses which were modelled i.
Mass flow rate of NOx, THC and CO
ii.
Fuel Flow rate
iii.
Smoke and Soot based on smoke meter readings.
The following work flow shown in Figure 5.2.1 was used to check for raw data consistency and create a model.
Check for any bend in the variation parameters (difference in set and demand channels) Analysis of the repetition point (For the measured channels which were not monitored during the test)
Creating models for the required channels
Analysis of the model quality using the statistical data of the generated model
Outlier identification through analysis of normal probability distribution of residuals Analysis of outlier effect using measured vs predicted plots
Note: Olive green blocks represent raw data analysis and blue blocks represent the model building stage.
Figure 5.2.1: Raw Data Analysis and Model Generation Work Flow
Model statistics and normal probability distribution plot for residuals for one of the modelled region for fuel flow and mass flow rate of NOx are shown in Figure 5.2.2 and 5.2.3. The model regressors are very good (R2adj and R2Pred > 0.95). This is because of emphasis given on getting high accurate data as explained before. The model statistics for other modelled channels were also found to be good. Figure 5.2.4 shows the intersection plot for NOx flow rate and fuel rate for one of the operating point with the 95% significance interval.
Figure 5.2.2: Measured vs Predicted Graphic
Figure 5.2.3: Normal Probability Distribution Graphic for Residuals 5.3. OPTIMIZATION AND VERIFICATION Optimization process involved applying stationary driving cycle algorithm to the ESC points for the selected sub variant with appropriate cycle weightage. The optimization was always run for minimum cycle SFC condition with cycle constraints on NOx, HC, CO and Soot. The local constraints (ECU Variation) for each operating point were also fixed with the maximum and minimum operating envelop of the engine operating space. Map smoothing was also used as a constraint. The predicted results were verified by running a verification test. Once the verification results were found to be at satisfactory levels, the whole of the emission region was calibrated to meet the random NOx tests.
6. RESULTS The predicted and measured plots of NOx, Smoke, HC, CO and fuel flow are respectively shown in Figure 6.1, Figure 6.2, Figure 6.3, Figure 6.4 and Figure 6.5. As evident from these figures, good correlation was observed between predicted and measured data. The predictions for the Cycle NOx and fuel flow rate were spot on. The predictions for the other modelled responses were excellent.
Figure 5.2.4: Intersection Graphic for Fuel Flow (FB_VAL_A) and NOx (MF_NOx_71) mass flow rate
Figure 6.1: NOx Comparison
Figure 6.2: Smoke Comparison
The other advantage which was also derived out of the Global Model was for shift 2 in emission points of ESC test cycle. These shifts in emission points resulted from a requirement of higher low end torque (about 20% increase in torque over existing level). Since the emission points depends on the position of location of 50% engine power on full load performance curve, the higher low end torque resulted in shifting of emission points. Because of the Global Model, this was easily accommodated, which would have not been possible otherwise. Fuel efficiency improvement of about 8% was derived on one of the variant when compared with already existing traditionally calibrated variant. This improvement was also seen on the vehicle. Other sub-variant showed 3-5% improvement in fuel consumption when compared with already existing calibration.
Figure 6.3: THC Comparison
Figure 6.4: CO Comparison
Figure 6.5: Fuel Flow Comparison
7. CONCLUSIONS The Global Modelling technique was found to be a very useful tool to map the entire engine operating region. The four sub-variants of the engine were calibrated to meet Euro III/BSIII emission norms within a time period of 305 hours running two shifts a day, which also included the time taken to run the model points. Fuel efficiency improvement of about 8% was seen on one of the variant, which was also validated on the vehicle. For other sub variants, fuel efficiency improvement of about 3-5% was seen. The shift in ESC Emission Points that resulted due to further increase in low end torque was also easily accommodated with the Global Modelling Technique.
2
The emission points or A,B and C speeds depends on the relative location of engine speed (NH) at 70% power (beyond peak power) and engine speed (NL) at 50% power (below peak power). A, B and C speeds are then calculated as linear combination of NH and NL
REFERENCES 1. Basics and beyond the basics, Cameo User’s Guide, AT5102E Rev 00, October 2013. 2. User’s guide advanced, i-Procedures for AVL Cameo 2013 R2, AT5102E Rev 00, October 2013. 3. Graybill, F. A. and Iyer, H. K., Regression Analysis – Concepts and Applications, Duxbury Pr, 1st edition.
CONTACT Anuroopa Varsha Manager Development, ERC Engines, Tata Motors Ltd. Pimpri, Pune – 411019 India. Mobile:+91-8237003739 Landline:+91-020-66134636 Email:anuroopa.varsha@tatamotors.com