Bandaru Balaji - Controls, Measurement & Calibration Congress
Efficient Calibration Approach – A Model Based Calibration of a Common Rail Direct Injection Diesel Engine Bandaru Balaji and L. Navaneetha Rao Ashok Leyland Ltd., India
ABSTRACT Due to high number of calibration parameters, the advanced technology diesel engines require an efficient calibration process to handle the system complexity and to avoid a dramatic increase in calibration costs. The current work presents on-line, time efficient calibration of High Pressure Common Rail (HPCR) direct injection system parameters of a Diesel engine with CAMEO intelligent procedures to meet the performance and emission targets. Various HPCR injection parameters, such as number of injections, start of main injection, quantity and timing of pilot injection and rail pressure were considered as DoE variables, and D-Optimal design was used for DoE test matrix. The DoE test points were executed through iProcedures while interfacing Testbed Automation system and Engine Control Unit. The measurement data was evaluated using inbuilt statistical tools. Global optimization of European Stationary Cycle was carried out to minimize the fuel consumption while meeting the emission targets. The optimized maps were verified on the testbed and the results were compared with the global optimization model results. Later, drive cycle based optimization was done by offline to improve the fuel economy and the fuel economy Is improved by 2.6% with significantly less testing cost and time.
INTRODUCTION Implementation of stringent emission norms and strongly increasing demand on engine fuel economy made OEM to conceive new engine technologies and emission strategies, which intern increasing the number of parameters/variables, system complexity, calibration time and testing cost. Due to the highly increased number of parameters and the need to reduce the calibration time and cost, manual tuning of the parameters is now replacing by mathematically assisted calibration procedure. Such a procedure is based on Design of Experiments (DoE) with associated modeling methods in order to reduce the number of tests used to build response models depending on variation parameters, and mathematical optimization techniques to determine the optimal values within the model design space [1-5]. However, with conventional DoE approach the screening is carried out manually and identification of the valid experimental domain by considering limit violations of engine parameters is very difficult and time-consuming task especially in high dimensional problems. Stuhler et al. [6] proposed a more efficient Adaptive online DoE approach which automatically identify the feasible design space and generate an optimal design in multidimensional variation spaces of irregular and unknown boundaries by considering all limitations. In order to perform the tasks in an efficient way, these mathematical techniques are generally associated with testbed automation system, requiring intelligent procedures and reliable test equipment [6-9]. Koegeler et al. [7] described a model based optimization of ECU parameters of Gasoline Direct Injection engine to minimize the fuel consumption while meeting the target NOx emissions. The results show that the global optimization using CAMEO improved the overall fuel economy with better NOX margins and enhanced drivability. The present work describes a model based optimization of a turbo-charged, HPCR injection system Diesel engine with CAMEO intelligent procedures (iProcedures) [5] in a instrumented testbed. Various calibration parameters like number of injections, Main Injection (MI) Timing, quantity and timing of pilot injection and rail pressure were investigated. D-Optimal experimental design technique [9] was employed to build a test matrix. The test matrix was executed through iProcedures while interfacing testbed automation system (PUMA) and Engine Control Unit (ECU) application system. Two cases of optimization targets were investigated in the current study.
Base optimization of ESC cycle to minimize fuel economy while meeting the emission targets Drive cycle based optimization to improve the fuel economy
EXPERIMENTAL SETUP
Figure 1: Engine Testbed Configuration
The engine testbed setup is shown in figure 1. The system features an testbed automation system (PUMA) with high performance, AC transient dynamometer. Engine inlet air pressure (P), temperature (T) and relative humidity (RH) were controlled by an Air Conditioning System. The instantaneous and cumulative fuel consumption were measured with high accuracy fuel flow meter (AVL 735S), while the fuel temperature before the fuel injection pump was regulated by fuel conditioner (AVL 753C). The engine thermal conditions were controlled by the testbed coolant temperature and charge air cooling systems. Engine was instrumented to analyze the engine behavior under the DOE test matrix. The testbed equipped with gas analyzer (HORIBA MEXA-7100) and opacimeter to measure the exhaust gas emissions.
CALIBRATION APPROACH A genuine manual local approach is still the most commonly used calibration process during engine development. This approach can be easy and effective with few control parameters. Because of complexity of the power train systems, the conventional calibration process becomes more tiresome and time consuming [1, 6-8]. The application engineer has to understand the behavior of the engine performance and emissions over the ECU control parameters throughout the whole calibration and optimization process, and then simultaneously optimize all the control parameters to meet the local or global objectives. With an objective to reduce the engine calibration efforts, a fully automated online calibration tool (CAMEO) was used to design, execute the test matrix efficiently and to support optimization tasks by building engine models and optimization in the multidimensional room. The global optimization of European Stationary Cycle (ESC) was carried out to minimize the fuel consumption while meeting the engine out emission targets. The different steps of the tool and ESC emission test order were synthesized in figure 2 and 3 respectively.
Figure 2: Calibration work flow
Figure 3: European Stationary Cycle
DESIGN OF EXPERIMENTS – TEST DESIGN AND EXECUTION Generally, the engine operating range is often restricted by limitations on injection timing, rail pressure, exhaust gas temperature, boost pressure and other parameters. The manually identification of the feasible design space is a critical task which requires lot of interpretations and testing time. In this aspect, CAMEO offers several DoE screening iProcedures such as Adaptive online DoE, Online DoE screening and Global DoE screening. In this study, a two layer – Adaptive online DoE iProcedure was adopted which recognizes the feasible design space by monitoring the multidimensional space to detect possible limit violations (screening phase) and then generates an optimal design inside the valid region (adaptive phase). Detail explanation can be found in literature [6, 9]. As per the procedure, all the operation points (13 modes of ESC) to be optimized were defined in the first layer. The full loads operating points (2, 8 and 10modes) were defined by speed/alpha (N/A) mode while the part load points were declared in speed/torque (N/T) mode. The following ECU variation parameters were investigated for each operating point.
Number of Injections Main Injection Timing Pilot Injection Timing Pilot Injection Quantity Rail Pressure
All the variation parameters and their range were defined in the subsequent layer along with a start point. This start point behaves as a center point from which the variations are ramped to the next variation set point. DoE test matrix (Test Design) was generated using D-optimal design with second order quadratic models. Then the test matrix was executed on the testbed (Test Execution) using fully automatic iProcedures while interacting with the,
Testbed automation system (PUMA) to control the dynamometer and to trigger and receive requested measurements ECU application systems to adjust ECU variation parameters such as maps, curves or single values in a step-bystep, ramp or gradient mode limit monitoring strategies with reactions to protect the engine while varying ECU labels
Figure 4: Online DoE screening at 2 operating point The figure 4 is a snap shot taken at the testbed in one of the operating points while running the DoE screening. The online DOE screening iProcedure moves in small steps beginning from the central point towards the next variation set point while checking limit violation (constraint) at each step. As soon as a limit violation occurs, the procedure executes the limit reaction by stepping back to the previous valid step and re-approach forward for a valid measurement point is found. However, due to the limit violations, the statistical correctness of the design is no longer guaranteed. In order to guarantee a high quality of the design, respectively the model, the procedure evaluates the quality that can be reached with the measured points [6]. If necessary, the procedure adapts the design by iteratively adding some additional points in the valid range for better quality. In this study, no limit violations were occurred during test execution and the black color points (â– ) indicate the valid measurement points without any limit violation. All the measurement points defined in the test run were recorded after the stabilization time.
MODELING AND OPTIMIZATION Case1 – Base ESC Optimization All the test bed measurements conducted during the test execution phase were evaluated using the inbuilt statistical tools. These tools support to check the plausibility of measured data by identifying possible measurement outliers, analyzing the distribution of variation points and checking the measurement stability via repeat points. The outliers in the measurements (marked in red circle) can be due to error in measurements. Such points can be deactivated or manipulated by measuring again on the testbed. The plausibility check routine compares the measurement results of test matrix with the measurement results of the first measurement at the repetition point. Figure 5 shows the measurement stability of charge air, fuel coolant temperatures and humidity ratio over the run order. The green color circular points represent the measurements at the repetition points and are useful to evaluate the measurement quality of the equipment over the complete test execution. 0
During the test matrix execution, the fuel temperature was maintained within the boundary conditions (34Âą1 C), but the trend shows a gradual increase and then stabilization. The coolant temperature was also fluctuating and some points were out of the boundary conditions. Manual evaluation is more time consuming and almost impossible for a large data. However, with the CAMEO statistical tools the data processing time was decreased tremendously.
Figure 5: Measurement data plausibility check After plausibility check, the mathematical models were generated using second order polynomials. The model quality and the goodness of model prediction were assessed using several statistical methods. The value of the coefficient of 2 2 2 determination (R ), the adjusted coefficient of determination (R Adj), the quality of prediction (R Pred) and other factors for the analysis of variance were calculated. Same approach was used to create models for other parameters. The figure 6 shows a fuel consumption model generated as a function of MI timing and Rail pressure and the statistical analysis results of the model.
a. Fuel consumption model
b. Statistical analysis of model prediction
Figure 6: Sample model generation and validation Figure 7 illustrates the intersection of the ECU variation parameters on the brake specific fuel consumption (BSFC), smoke, NOx and CO emissions at 2 operating point. With this graph it is possible to investigate the influence of one variation parameter on the response, while other variation parameters are fixed. The blue color lines represent the generated model curves. The design space and the confidence intervals with a confidence level of 95% are shown in pink and dashed green lines. The results show that both MI timing and rail pressure have significant influence on BSFC and NOx. However, the MI timing influence was more predominant than the rail pressure. On the other hand, increasing pilot injection quantity and timing causing more CO emissions and marginal influence on BSFC and smoke.
Figure 7: Intersection plot of variation parameters After modeling the parameters global optimization was carried out to meet the objectives. In case 1, our optimization target was to minimize the overall fuel consumption and bringing emissions (NOx, CO and HC) below the engineering targets. An off-line global optimization of the all the operating points (13 modes) was carried out while meeting all the constraints. Optimum ECU variation parameter values were obtained for each operating point. With the obtained optimum values, ECU maps were generated and validated on the testbed against the model output results. The percent deviation between the model output results and testbed results were plotted in figure 9. The testbed validation results are well matched with the global optimization output results.
Figure 8: ESC optimization results
CASE 2: Drive cycle based optimization Drive cycle development The drive cycle data was measured on instrumented vehicles under real world traffic conditions of Chennai, India. Multiple engine and vehicle parameters were logged from the Electronic Control Unit (ECU) and auxiliary instruments fitted to the vehicle at high frequency up to 100Hz frequency. GPS system was installed for continuous monitoring of the co-ordinates of the vehicle. Intense care was taken while mounting the fuel flow meter to subside the fuel pressure fluctuation and for accurate measurements. In current study, three drive cycles measured in different routes of Chennai are examined.
Figure 9: Measured vehicle and Engine drive cycle data in Chennai The engine drive cycle, in terms of engine speed and torque, is extracted from the measured drive cycle. The vehicle velocity profile and corresponding engine drive cycle data are represented in figure 9. Both the vehicle and engine drive cycles were analyzed using MATLAB programming and necessary corrections were made in the engine drive cycle before simulating in testbed. The corrected engine drive cycle data (N/T) was interfaced to a transient dynamometer through testbed automation system (PUMA) for simulating in testbed. The simulated drive cycle (demand) and actual engine response were closely monitored and regression analysis of the engine response values on the demand values are performed for speed, torque 2 and power. The correlation coefficient (R ) > 0.9 is considered as criteria for the test validation. Intense care was taken to simulate the actual thermal characteristics of the engine during on-road driving cycles into the engine testbed for accurate predictions. Finally, the instantaneous and cumulative fuel consumption was measured over the drive cycle and compared with the vehicle experimental data. The drive cycles recorded in different routes were simulated and results are tabulated in table 1. The testbed simulation results are closely matching with the vehicle results. Drive Cycle
Testbed simulation result (kmpl)
Vehicle result (kmpl)
Route 1
3.72
3.69
Route 2
3.71
3.65
Route 3
4.32
4.25
Table 1: Drive cycle simulation results
Figure 10: Bubble chart analysis of engine drive cycle All the drive cycles recorded in different routes were combined which represent the overall driving pattern of the vehicle in the city. The combined drive cycle was analyzed and most vehicle operating zones were identified. The ESC mode points corresponding to the most vehicle operating zones were noted for re-optimization. Bubble chart analysis of the drive cycle is represented in figure 10. From the analysis, 2, 6, 7 and 8 mode points are considered for re-optimization. The objective of the re-optimization was to tune the ECU control parameters to improve the fuel economy over the drive cycle. Already generated CAMEO models were used to re-optimize the ECU parameters. Then off-line global optimization was done by giving high priority to the selected ESC modes and prepared optimum maps without incurring further testing cost and time. Later the optimum maps were tested for fuel consumption in a transient testbed over the same drive cycle. Average value of three tests is plotted in figure 11 and with drive cycle based optimization fuel consumption is improved by 2.6%.
Figure 11: Drive cycle based fuel consumption results
CONCLUSION A model based calibration approach was implemented to calibrate and optimize various ECU parameters to minimize the target function (fuel consumption) while meeting engineering emission targets. The optimized maps were verified on the
testbed and the results are matching with the global optimization model results. Due to the automated DoE Screening iProcedures in CAMEO tool the human efforts have been reduced considerably while effectively utilizing the testbed resources. Moreover, with already generated models drive cycle based re-optimization was carried out offline and achieved 2.6% improvement in fuel economy with significantly less testing cost and time.
ACKNOWLEDGMENTS The authors would like to acknowledge the Head – Engine R&D and testing team for providing the necessary resources
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CONTACT INFORMATION Bandaru Balaji Manager - Engines PD balaji.bandaru@ashokleyland.com Ashok Leyland Technical Center Vellivoyal Chavadi, Chennai – 600 103 India
DEFINITIONS/ABBREVIATIONS DoE HPCR MI NOx CO HC BSFC ESC
Design of Experiments High Pressure Common Rail Main Injection Nitrogen Oxides Carbon monoxide Hydrocarbons Brake Specific Fuel Consumption European Stationary Cycle
L. Navaneetha Rao Divisional Manager - Engines PD navaneetharao.L@ashokleyland.com Ashok Leyland Technical Center Vellivoyal Chavadi, Chennai – 600 103 India