Steady State Calibration for Catalyst Heat-up Optimization on Gasoline Direct Injection Engine

Transcription

1 Keihin Technical Review Vol.4 (2) Technical paper Steady State Calibration for Catalyst Heat-up Optimization on Gasoline Direct Injection Engine ガソリン 直 噴 エンジンにおける 触 媒 昇 温 の 定 常 適 合 Jing HE *1 Fuyuki KAKIMOTO *2 Fuminori SATO *1 何 静 柿 本 冬 樹 佐 藤 史 教 Carsten HAUKAP *3 Thomas DREHER *3 近 年, 低 燃 費 と 高 出 力 を 両 立 するために, 過 給 直 噴 ガソリンエンジンの 需 要 が 高 まっている. 噴 射 制 御 自 由 度 の 高 さにより, 特 に 冷 間 始 動 時 の 早 期 触 媒 昇 温 効 果 が 期 待 できる 反 面, 直 噴 エンジン 特 有 の 煤 発 生 に 対 して も 考 慮 が 必 要 であり, 適 合 は 非 常 に 難 解 となっている. 従 来, 触 媒 昇 温 適 合 には, 高 度 な 計 測 環 境, 複 雑 なデー タ 処 理 技 術 が 要 求 される 過 渡 適 合 を 用 いるが, 我 々は, 定 常 ベンチ 環 境 による 触 媒 昇 温 の 適 合 手 法 を 考 案 した. IAV 社 の EasyDoE Toolsuite を 用 いて,DoE(Design of Experiments) モデリング 最 適 化 をおこなった. 本 試 験 は,エンジン 回 転 数 および 負 荷 を 固 定 し,ローカル DoE を 計 画 した. 煤 の 排 出 低 減 効 果 をねらって,イン ジェクター 噴 射 回 数 は 最 大 4 段 までのテストを 実 施 した. 点 火 タイミングと 噴 射 時 間 のトレードオフ 関 係 によ り, 噴 射 回 数 が 強 く 制 約 を 受 ける.このような 複 雑 な 運 転 領 域 を, 効 率 的 に 燃 焼 限 界 まで 探 索 する 為,IAV 社 の Boundary Finder シーケンスを 採 用 した. 最 大 6パラメータの 領 域 探 索 を 約 6 時 間 で 計 測 できた. また,モデリング 手 法 は (Gaussian Process Models)および HILOMOT(HIerarchical LOcal MOdel Tree)などを 採 用 している. 結 果 として, 排 気 エネルギーとエミッションの 多 目 的 最 適 化 において, 従 来 条 件 より 早 期 触 媒 昇 温 とエミッション 低 減 を 両 立 することができた. Key Words: Steady state calibration, Catalyst heat-up, DoE, Optimization, Gasoline direct injection engine 1. Introduction The discharge of clean exhaust gas from cold start to catalyst heat-up is very important to satisfy strict emission regulations. The particulate matter discharged from engine has detrimental effect on the aerial environment and the human body. Directinjection engines particularly exhaust more soot than port-injection engines. In recent years, various countries have strengthened exhaust gas regulations for gasoline engines. Furthermore, for obtaining higher engine power and reduced fuel consumption, supercharged direct-injection engines are necessary, which makes control system more complex and requires much effort for engine calibration. On the other hand, to respond to diversified needs in an increasingly competitive automobile market, cost reduction measures are must be incorporated with the maximum use of existing facilities and the further pursuit of efficiency of development. For the purpose of using steady state calibration to early catalyst heat-up for optimized emissions, it is necessary to cope with both compensation of engine speed variation according to ignition timing retard and early catalyst heat-up. The maximum nine control parameters, such as ignition timing, starts of the injection, intake and exhaust camshaft phasing, and lambda, are adopted for this study. However, Received 4 September 2, Reprinted with permission from IAV GmbH, original publication in, Proceedings of the 8th Conference Design of Experiments (DOE) in Engine Development, June 11-12, 2, Berlin. Copyright 2 IAV GmbH. *1 Development Department, R&D Operations *2 Keihin Sales and Development Europe GmbH *3 IAV GmbH -17-

2 Steady State Calibration for Catalyst Heat-up Optimization on Gasoline Direct Injection Engine because of interaction among these many parameters, so many parameters make convex hull very complex. Furthermore, measurement start point of boundary search largely depends on the result of convex hull. Additionally, in order to maintain balance in exhaust gas enthalpy, fuel consumption and exhaust emission, decision of objective functions and constraint conditions become very difficult. This paper introduces techniques for solving these problems. 2. Catalyst heat-up optimization In general, the calibration of the catalyst heatup is very complex and time consuming. Complex, because the optima has to be a well-balanced compromise between exhaust gas enthalpy to reach the catalyst light-off as fast as possible, gaseous emissions as well as soot emissions, drivers comfort and finally the fuel consumption. Time consuming, because the number of calibration parameters is very high, especially with the introduction of multiple injections and each experiment usually must start at the same cold engine conditions. This chapter introduces an approach to optimize calibration parameters using the steady state DoE methodology. The core assumptions for the experiment as well as the test environment and equipment are described Development task and model assumptions Figure 1 shows typical measurements of emission, catalyst temperature and engine speed right after the engine start for a given standard vehicle cycle. The typical calibration task is to find optimal engine parameter settings after engine idling to heat-up the catalyst idealistically to reach light-off temperatures. The core task of this development is to generate engine out models of the exhaust heat amount (exhaust enthalpy), temperatures, emissions, fuel consumption and other important engine outputs. For economic reasons a steady state DoE approach will be used. For this and as one main core assumption, the engine out conditions after engine idling are defined to be constant and persistent after engine start. Thus, the models will describe the main single influence of the calibration parameters on engine outputs, only. The purpose of the models is to find optimal settings for the steady state engine maps and to evaluate different calibration strategies. An important focus of the development is to perform all measurements on a common engine test bench with a much reduced effort compared to cold start experiments Engine and test bench configuration The engine used in our calibration tests is a turbo charged, direct-injection gasoline engine. Figure 2 illustrates the system structure of the test bench. This experiment uses the A&D itest 3.2 data acquisition and control system for dynamo control. The automatic operation sequence is prepared using the A&D ORION (Open Real-time Intelligent Optimization Network) automated measurement software, to conduct boundary detection measurements and DoE measurements. In order to protect engine during automatic operation, the knock indication system KIS4 is adopted for knock Figure 1 Find optimize engine settings to increase exhaust enthalpy (Constant engine speed) Engine Speed NOx THC Catalyst Temperature Time First seconds after engine start for a common vehicle cycle -18-

3 Keihin Technical Review Vol.4 (2) detection and combustion fluctuation observation in real time. The AVL483 micro soot sensor is used for soot measurement, and the MEXA-71 analyzer is used for exhaust analysis. The A&D CHOE-2 heat exchanger device is used to control coolant temperature. Furthermore, engine control system uses the A&D ADX-43 equipped with Keihin s original application instead of a mass production ECU and software. The drivers of the injectors and the fuel pump are specially manufactured for test engine Task definition One aim of the development is to compare and evaluate different injection strategies for optimizing split injection patterns of, single injection, double injection, triple injection and quadruple injection. Because the injector specification allows up to four injections, a model for each injection pattern will be created. The start of injection will be one main input parameter of the DoE models. For each injection pattern, the injected quantity will be equally distributed for all injections. Furthermore, the fuel pressure, ignition timing, lambda, intake and exhaust camshaft phasing are also defined as model inputs. Figure 3 summarizes the main in- and output parameters of the DoE models. In order to take engine coolant conditions into account, the experiments will be conducted with a EasyDoE DMS MEXA-71 AVL-483 CHOE-2 Offline ORION ASAP3HS itest CAN Dynamo A&D KIS4 KISTLER Gasoline DI Engine (Turbo Charged) ASAP3 UDP Engine Control ADX-43 Throttle Intake valve Exhaust valve Lambda Waste gate Ignition Injector Device Driver Fuel pump Device Driver Figure 2 Test bench structure Intake camshaft phasing Exhaust camshaft phasing Lambda Ignition timing Fuel pressure SOI 1 st, SOI 2 nd, SOI 3 rd, SOI 4 th Temperatures Exhaust enthalpy NOx THC Soot/PN COV Fuel consumption (BSFC)... Coolant temperature Figure 3 Model in- and output parameters -19-

4 Steady State Calibration for Catalyst Heat-up Optimization on Gasoline Direct Injection Engine constant water temperature (coolant out) of approx. 3 C. The engine speed is kept at rpm and load is kept at 3Nm. 3. Steady state calibration process This chapter describes the steady state model based calibration process used for catalyst heatup optimization. The first step of the process, e.g., the definition of target goals of the development, definition of base parameters, general conditions, constraints. The experimental setup has been specified in the previous chapter. The remaining steps, listed below, will hereafter be discussed in detail: Boundary detection, Test design, Data evaluation and modelling building, Optimization and validation Boundary detection In order to generate data driven models of high quality, the definition of the design space and the parameter ranges is essential. The better the design space is known in advance, the better the coverage of candidates will be for the measurements and the later model building. Furthermore, if restricted and not drivable areas can be omitted from the design space beforehand, the more efficient and less time consuming the measurement effort will be, because hard limit violations are avoided. To cope with the nonlinear dependencies and constraints of the input parameters, the test design will be calculated using a convex hull of the input parameters. This convex hull will be created on the base of measurements done with an automated Boundary Finder sequence of the test bench automation software ORION. Because the identification of a design space is extremely time consuming for nine parameters in case of a quadruple injection pattern, the dimensions of Table 1 Control parameters and their ranges for boundary detection Control parameter Ignition timing SOI_1st SOI_2nd SOI_3rd SOI_4th Fuel pressure Unit CA CA CA CA CA MPa Range ~ -3 ~ ~ -3-6 ~ ~ ~ 2 the boundary detection will be reduced. Table 1 summarizes the control parameters and ranges chosen for the boundary detection. For all remaining input parameters constant values will be chosen for the boundary detection. For the later test design calculation the ranges will be defined manually. The ignition shows a strong influence on exhaust temperature, therefore the ignition is one main parameters of boundary detection. The starts of the injections (SOIs) have a strong geometrical dependency among themselves and are highly influencing emissions, especially soot particles in size and number. The fuel pressure strongly influences the injection rate. Furthermore, the injection rate is limited by a minimal flow rate of the injectors, refer Figure 4. Because of the strong dependency of the ignition timing and injection time on one hand, as well as the geometrical dependencies of injection times in combination with the fuel pressure on the other hand, the boundary detection is greatly dependent on the initial start point of the boundary search. To cope with these effects, a short pre-investigation has been conducted to determine the size and shape of the convex hull in dependency of the initial start point. The determination has to be done carefully for all parameters which are constant during the boundary search. Figure shows the image and mode of operation of the Boundary Finder sequence of ORION. There are three steps for boundary detection: -2-

5 Keihin Technical Review Vol.4 (2) Constraint 36 Injection Time Minimal flow of injector Start Pont SOI_2nd Measured Point DoE plan Point Constraint re Ignition timing adv SOI_1st 36 Figure 4 Dependency of injection time on ignition timing and geometrical dependencies of multiple injections Step1 Move to a safe start point in dependency of the last boundary point; Step2 Move to an intermediate point inside convex done with reduced setting speed. Figure 6 shows the convex hull results for the injection patterns of single injection, double hull of already measured boundary points; Step3 Search a new target boundary point starting from the intermediate point The intermediate point guarantees the optimal start conditions for each adjustment and boundary search. The speed of the parameter adjustment is dependent on the actual operation. Moving inside Initial Start Point Boundary Point Start Search Intermediate Target Next Boundary Steps the hull to an already known intermediate point is very fast. Whereas the actual boundary search as well as a re-approach in case of a limit violation is Figure Image of Boundary Finder Single Injection Double Injection SOI_1ST [deg] Ignition [deg] Fuel pressure [MPa] SOI_1ST [deg] Ignition [deg] Fuel pressure [MPa] Triple Injection Quadruple Injection SOI_1ST [deg] Ignition [deg] Fuel pressure [MPa] SOI_1ST [deg] Ignition [deg] Fuel pressure [MPa] Figure 6 Convex hull result for different injection pattern -21-

6 Steady State Calibration for Catalyst Heat-up Optimization on Gasoline Direct Injection Engine Table 2 Summary of design points, validation and repetition measurements Injection pattern Single Double Triple Quadruple DoE design points Validation points Repetition points injection, triple injection and quadruple injection. The convex hull size of quadruple injection is approximately half of single injection. Furthermore, the results demonstrate strong dependency of fuel pressure on the number of injections. The single injection pattern allows a much higher fuel pressure level than the multi-injection patterns. This fact can be explained by system limitations, which request a minimal injection rate and allows an equidistant distribution of the injected fuel mass per injection, only. This example illustrates the advantage of an automated boundary detection methodology. In the present case, a run time of six hours was required for a six parameter boundary detection Test design To ensure maximal accuracy, one model is created for each injection pattern at constant operating conditions of speed of rpm and load of 3Nm. Furthermore, the coolant out temperature is defined to be constant at 3 C. The introduced convex hulls of the automated boundary detection are the base of the constraints. For all remaining parameters the constraints and ranges are defined manually. The following parameters are defined as input of the DoE, refer Figure 3. Intake Camshaft phasing, Exhaust Camshaft phasing, Lambda, Ignition timing, Fuel Pressure, SOI 1 st SOI 4 th. The following design methodology is used to calculate space filling test designs (1) : Initially, a normalized design space is generated taking all defined ranges, constraints and convex hulls into account. Within this design space a candidate set is generated using a Sobol sequence. Finally, the target number of design points and the distribution is the result of a combination of Latin sampling and distance optimization. This procedure assures a very good distribution at a minimal calculation time. One important advantage of this procedure is its extensibility. Important, because the estimation of required measurements is very difficult, especially for designs of high order. The test plan can be enlarged very quickly with condensing of the existing design. This has the advantage of easily adding measurements until the target model quality is reached. For statistical evaluations and validation, additional validation and repetition measurements are planned automatically. The used test design methodology has been shown to be the best practice for the most important model types for a steady state DoE approach: Gaussian Process Models, HILOMOT Hieratical Local Model Tree, RBF Radial Basis Functions and Polynomials (2), (3) Data evaluation and model building After carefully evaluating the measurements for each injection pattern the models are created. For this model building process, the following model types have been used: Gaussian Process Models, HILOMOT models, Radial Basis Function and Polynomials (using various regression algorithms). The final model is either chosen manually or using an automated model building process. The latter includes an automated outlier detection as well as -22-

7 Keihin Technical Review Vol.4 (2) Table 3 Model results Injection Number Single Injection Double Injection Triple Injection Quadruple Injection Model Exhaust Enthalpy Flow BSFC COV NOx THC Soot Exhaust Enthalpy Flow BSFC COV NOx THC Soot Exhaust Enthalpy Flow BSFC COV NOx THC Soot Exhaust Enthalpy Flow BSFC COV NOx THC Soot Unit [kw] [g/kwh] [ppm] [ppmc] [mg/m^3] [kw] [g/kwh] [ppm] [ppmc] [mg/m^3] [kw] [g/kwh] [ppm] [ppmc] [mg/m^3] [kw] [g/kwh] [ppm] [ppmc] [mg/m^3] nrsme (Fitting) 3.3%.9% 8.% 2.2% 2.% 3.7% 2.%.2% 6.7% 2.6% 2.8% 3.3% 2.4%.3% 8.7% 2.% 3.1% 2.1% 2.%.% 7.8% 3.3% 3.2% 3.8% nrsme (Validation) 2.9%.% 8.6% 4.6% 4.%.3% 2.% 7.7% 8.6% 3.6% 2.9% 6.7% 2.9% 6.9% 1.9% 3.1% 4.2% 4.1% 2.% 4.8% 9.% 3.7% 3.% 7% nrsme (PRESS) 3.% 6.2% 9.1% 3.6% 4.1% 6.8% 2.8%.4% 9.4% 3.% 4.% 9.4% 3.1%.8% 1.8% 3.4% 4.3% 3.4% 2.8%.% 1.7% 4.1% 4.% 7.8% Model Type Polynomial Polynomial Polynomial Polynomial HILOMOT HILOMOT HILOMOT Polynomial automated BoxCox output transformation. The final model type is chosen on the base of weighted fitting errors, validation errors and PRESS, whereas the errors are calculated as followed: RMSE = 1 n n i = 1 ( y i yˆ i ) 2 (1) n 2 PRESS = ( yˆ i y i ) (2) i = 1 with y i : Measured value ŷ i : Prediction value Exhaust enthalpy flow is calculated using equation (3). EnthalpyFlow EX = T EX Cp EX MassFlow EX (3) with T EX : Exhaust temperature Cp EX : Specific heat constant pressure MassFlow EX : Exhaust mass flow and the normalized RMSE as followed: nrmse RMSE = 1 (%) Max Min (4) with Max: Maximum measurement value, Min: Minimum measurement value Table 3 shows an overview of the chosen model types and model errors for each injection pattern. The overall model quality is good and the relation of fitting, validation and PRESS nrmse shows the expected distribution. As the sole exception, the models for the coefficient of variance (COV) are showing noticeable higher errors. One root cause for the behavior of -23-

8 Steady State Calibration for Catalyst Heat-up Optimization on Gasoline Direct Injection Engine COV is the retarded center of combustion close to misfire for late ignition. Especially for high numbers of injections a more retarded ignition is required to fulfill the constraint of the minimal injection rate of the used control system. Figure 7 shows the predicted vs. observed plot as an example for a single injection pattern Optimization and validation The first objective of the evaluation is the comparison of the four injection patterns. For this, the Pareto fronts for various parameter combinations are calculated. With plotting the resulting trade-offs, the potential of each injection pattern can easily be shown. To create the Pareto a full factorial grid is calculated using the same distribution for each input parameter. Figure 8 shows the results for chosen engine outputs on the ordinate over the abscissa lambda. Lambda has been proven to be a good choice for the comparison. Predicted 2 1 COV (Single Injection) Fit Point Validation Point 1 2 Measured Figure 7 COV model of triple injection 28 Single Double Triple Quadruple Single Double Triple 12 Quadruple Enthalpy Flow_Ex [kw] NOx [ppm] Single Double Triple Quadruple Single Double Triple 2 Quadruple Cov_Cyl1 BSFC [g/kwh] THC [ppmc] Soot [mg/m^3] Single Double Triple Quadruple Single Double Triple Quadruple Figure 8 Comparison of the four injection patterns using a Pareto optimization -24-

9 Keihin Technical Review Vol.4 (2) For rich engine operation, the highest exhaust gas enthalpy flow can be achieved using a single injection pattern and the lowest using the quadruple pattern. For stoichiometric and lean engine operation this behavior is reversed. The double injection pattern shows the highest efficiency, with the smallest break specific fuel consumption (BSFC). Here, the quadruple injection leads to the highest fuel consumption, whereas double and triple injection shows almost the same consumption. The quadruple injection shows the lowest NOx emissions with a reduction of approx. 3% compared to the single injection pattern for stoichiometric engine operation. For the THC and soot emission, the result are not as clear as for the NOx, but single and quadruple injection show almost the same level of emission. To conclude, one reason of the advantage of the quadruple compared to the singe injection pattern is that the quadruple injection pattern allows an engine operation with a more retarded center of heat release. Hence, an increased BSFC for quadruple injection at larger values of COV compared to the single injection pattern. However, the more retarded the combustion the lower the combustion temperature and thus the lower the NOx emissions. For this study, the Pareto front does not show the absolute optima. But it shows the maximal potential and the direct impact on results, if the parameter range and outputs are constrained. For the comparison shown in Figure 9, the COV is introduced as constraint at the point of infection of the gradient of the exhaust temperature. Additionally, the chosen COV values are still acceptable in terms of customers comfort. The intake and exhaust camshaft phasing show a strong influence on the engine outputs. Nevertheless, the hydraulic drive of the engine is limited in operation of a cold engine. Thus additional constraints for the camshaft phasing and ranges are defined. All further evaluations have been done using the shown constraint definitions. The target of this validation is to find optimal settings for each injection pattern and to compare these optima with the original map settings as Enthalpy Flow_EX [kw] Single_Point Double_Point Triple_Point Quadruple_Point BSFC [g/kwh] Single_Point Double_Point Triple_Point Quadruple_Point COV 3 COV T_CAT [ºC] Single_Point Double_Point Triple_Point Quadruple_Point T_Ex [ºC] Single_Point Double_Point Triple_Point Quadruple_Point 6 4 COV COV Figure 9 Pareto front analysis -2-

10 Steady State Calibration for Catalyst Heat-up Optimization on Gasoline Direct Injection Engine Table 4 Deviation of the double injection pattern compared to original reference engine maps Percent Injection different pattern Double Enthalpy Flow 9 BSFC -.8 COV 4 NOx -4.3 THC -27 Soot -38 Enthalpy Flow_EX [kw] NOx [ppm] BSFC [g/kwh] THC [ppmc] Soot [mg/m^3] COV Figure 1 Optimization results for each injection pattern reference. Figure 1 summarizes the best results of all optimizations performed for each injection pattern plotted on the abscissa. The red dotted line marks the engine outputs of the reference maps. If the enthalpy flow of exhaust gas is chosen as measure for the comparison, the double injection shows the best results of all patterns. Furthermore, the results for the double injection are even better than the reference of the original engine settings. At the same level of fuel consumptions compared to the reference, the double injection pattern shown a higher level of enthalpy flow and lower levels of emissions. Only, the COV value increases slightly. Table 4 summarizes the deviations of the double injection pattern compared to the original reference settings of the engine. 4. Conclusion This paper described the steady state calibration method with common engine test bench for catalyst heat-up optimization. In order to compare different injection strategies, a maximum number of six parameters were set in the boundary detection test. The results demonstrate strong dependency of fuel pressure on the number of injections., HILOMOT, RBF and Polynomials method were used to build models. All models were of high quality. Compared to reference maps, to establish quick heatup of the catalyst and lowering emission compatibly by multi-objective optimization of exhaust enthalpy and exhaust emission, has been achieved. References (1) Haukap, C.; Hegmann, M.; Kohler, B. U.: Strategies for improving the process of Test Design and Test Plan computation for high dimensional designs. In: 6 th Conference on Design of Experiments, Berlin. 211 (2) Baumann, W.; Dreher, T.; Ropke, K.; Stelzer, S.: DoE for Series Production Calibration. In: 7th -26-

11 Keihin Technical Review Vol.4 (2) Conference on Design of Experiments, Berlin. 213 (3) Hartmann, B.; Baumann, W.; Nelles, O.; Axes- Oblique Partitioning of Local Model Networks for Engine Calibration. In: 7th Conference on Design of Experiments, Berlin. 213 Authors Steady state calibration method efficiently implemented on engine test bed has been substantiated in this practice, and effort will be made to verify on actual vehicle hereafter. Furthermore, steady state calibration method will be effectively applied to Keihin s component development. Here, I would like to express my thankfulness to all persons in our company as well as IAV GmbH for their kind cooperation. (HE) Dr. Carsten HAUKAP Member of the department Methodology Development of IAV GmbH since 24. Specialist for steady state and dynamic DoE and processes and model based calibration. Responsible of project management of methodology related projects. Thomas DREHER Member of the department Methodology Development of IAV since 2. Specialist for test bench automation and model based calibration. As team manager responsible for software tool development. Software products: IAV EasyDoE and ORION (in cooperation with company A&D) -27-