ABSTRACTA new cost-based control strategy is presented that optimizesengine-aftertreatment performance under all operatingconditions. This Integrated Emission Management strategyminimizes fuel consumption within the set emission limits byon-line adjustment of air management based on the actualstate of the exhaust gas aftertreatment system. Following amodel-based approach, Integrated Emission Managementoffers a framework for future control strategy development.This approach alleviates calibration complexity, since itallows to make optimal trade-offs in an operational costsense.
The potential of the presented cost-optimal control strategy isdemonstrated for a modern heavy-duty Euro VI engine. Thestudied diesel engine is equipped with cooled EGR, VariableGeometry Turbocharger, and a DPF-SCR aftertreatmentsystem. A simulation study shows that the proposedIntegrated Emission Management strategy accomplishes 2%to 3% reduction in fuel consumption and operating costscompared to a baseline strategy. Further potential benefitsinclude reduced heat rejection associated with the EGRsystem and reduced DPF regeneration frequency.
INTRODUCTIONModern emission legislation requires ultra low emissions fora broad range of diesel powertrain applications, includingcommercial vehicles, heavy-duty trucks and non-road mobilemachinery. These near zero emission levels have to beaccomplished not only on a type approval test cycle, but alsoduring real-life operation; there is growing attention for in-use compliance, including Not-To-Exceed (NTE) areas,demanding On-Board Diagnostics requirements and In-UsePerformance Ratio (IUPR) monitoring. The next challenge
ahead for the industry is the reduction of CO2 emission fromroad transport [1]. This will lead to the introduction of newtechnologies, which will further complicate powertraincontrol design.
The main challenge in powertrain control is to find anoptimal, cost-efficient balance between fuel efficiency anddriveability within the boundaries set by emission legislation.With the growing number of actuators, sensors andsubsystems, it is no longer straightforward to optimize theoverall powertrain performance. Powertrains have becomeincreasingly complex from a hardware perspective as well asfrom a control software standpoint. More precisely, engine,drivetrain and aftertreatment subsystems show considerableinteractions and interdependencies, which are hard to fullycomprehend.
Up till recently, engine and aftertreatment development wasconducted separately with individual targets set for bothengine emissions and aftertreatment performance. Interactionbetween the engine and aftertreatment control systems waskept to a minimum. As emission limits get lower and aspressure to assure low real-life emissions under any operatingcondition increases, it is generally perceived that performancecan be improved by exploiting the synergy between engineand aftertreatment system. Improved aftertreatmentperformance, increased robustness, fuel savings andimproved durability are amongst the potential merits. In2010, the first systems with rudimentary control interactionhave been introduced to the market. EPA 2010 enginecontrollers switch between several discrete modes; e.g.thermal management and fuel efficiency mode. However, acontrol strategy that continuously seeks an optimum foroverall powertrain performance under varying real-worldoperating conditions still appears to be missing.
Integrated Emission Management strategy for cost-optimal engine-aftertreatment operation
This work presents a structured, innovative approach fordesigning and calibrating control strategies, that optimallyexploits the interactions between engine and aftertreatmentsystems: Integrated Emission Management (IEM). Theapproach is rooted in optimal control theory, and it has anintuitive interpretation in terms of operating costs. Contraryto other results found in literature, e.g. [2, 3, 4], this approachdeals with the synergy between engine and aftertreatmentsystem and focuses on minimizing exploitation costs.
The purpose of this work is twofold:
1. introduction of the general Integrated EmissionManagement framework, and
2. demonstration of an Integrated Emission Managementstrategy applied to a simulation model of a Euro VI heavyduty powertrain. The Integrated Emission Managementstrategy optimizes engine-out NOx emissions by on-lineadjustment of air management based on the actual state of theexhaust gas aftertreatment system. Benefits will bedemonstrated in terms of: fuel economy, operating costs andheat rejection.
INTEGRATED EMISSIONMANAGEMENTTo meet EPA 2010 and Euro VI emission legislation, mosttruck manufacturers will rely on engine measures (ExhaustGas Recirculation, advanced fuel injection equipment andturbo charging) in combination with exhaust gasaftertreatment systems (Diesel Particulate Filter (DPF), urea-based Selective Catalytic Reduction (SCR) system). Up tonow, the desired performance is achieved by a timeconsuming calibration process [5]; setpoints are determinedseparately for each controller through extensive enginetesting. Using Design-of-experiments (DOE) and off-linecalibration tools, optimal control settings are specified, suchthat the engine realizes the desired fuel consumption andemissions and the SCR system realizes the desired NOxreduction within the set ammonia slip limit. Engine andaftertreatment calibration are often optimized in separateiterations. Furthermore, safety margins are applied to makethe engine robust for varying operating conditions, e.g.varying ambient conditions, component wear and aging,varying applications and duty cycles. This sets limits to theachievable fuel consumption.
Future powertrains require an optimal and robust controlsolution that maximally exploits the interactions betweenengine and aftertreatment systems and that deals with real-world operating conditions. Due to the growing complexity, itis no longer straightforward to optimize overall systemperformance and the calibration effort has increasedexponentially during the last decade. Therefore, it hasbecome inevitable to introduce an integrated systemapproach: Integrated Emission Management.
Integrated Emission Management is a model-based controldesign philosophy for combined engine-aftertreatmentsystems that:
• minimizes fuel consumption, while meeting emissionconstraints;
• offers a robust emission control solution for both test cyclesand real-life operation;
• deals with complex system interactions;
• uses models and optimal control theory to derive optimalcontrol strategies;
• relaxes the calibration complexity.
In this systematic approach, the performance of the separatelow-level controllers is coordinated by a supervisorycontroller, as illustrated in Figure 1. Based on information ofthe actual status and the driver's torque request, this controllerdetermines the desired control settings for the different low-level controllers using on-line optimization. With theavailable prediction of possible NOx and PM reduction of the
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DPF-SCR system, the Integrated Emission Managementstrategy specifies the engine settings that give the requiredexhaust gas temperature, EGR and air flow, and emissions tominimize fuel consumption (and operational costs) within thelimits set for tailpipe emissions.
OPTIMAL ENGINE-AFTERTREATMENT INTERACTIONTo illustrate the concept and its potential, an IntegratedEmissions Management strategy is designed and simulatedfor a heavy-duty long-haul Euro VI truck application. Thisstrategy coordinates engine air management and urea dosingcontrol. This section discusses the studied engine-aftertreatment system and the developed control strategy inmore detail. The new strategy's performance is comparedwith a baseline strategy.
SYSTEM DESCRIPTIONPowertrain LayoutThe studied powertrain comprises a 340 kW 12.9 liter 6cylinder engine equipped with cooled Exhaust GasRecirculation (EGR), Variable Turbine Geometry (VTG), andcharge air cooler. The exhaust line contains a DieselOxidation Catalyst (DOC), Diesel Particulate Filter (DPF), a32.6 liter Cu-Zeolite SCR catalyst, and Ammonia Oxidationcatalyst (AMOX). The DPF can be regenerated activelythrough oxidation of diesel fuel injected upstream of theDOC. The powertrain outline is shown in Figure 2. Thesystem is capable of accomplishing EPA 2010 and Euro VItailpipe emission levels.
Figure 2. System layout of studied powertrain
Engine and Aftertreatment ModelsA mean-value engine model [6] is available for the describedengine. This mean-value model describes pressure,temperatures and mass flows throughout the engine based ona mix of fundamental thermodynamic principles, mass andenergy balances and component maps (e.g. compressor andturbine maps). More information on this mean-value enginemodel is found in [7, 8]. Prediction of NOx and PM emissionis incorporated into the model through empirical relationsdependent on O2 concentration in intake manifold and air/fuelratio λ, respectively.
Exhaust gas aftertreatment is simulated using a library ofaftertreatment component models. This library includes one-dimensional first principle models of pipe sections, DOC,DPF, SCR and AMOX. All component models predictthermal behavior and chemical reactions as well as relevantadsorption and desorption phenomena. A detailed overviewand model validation results of the catalyst models arepresented in [9].
INTEGRATED EMISSIONMANAGEMENT STRATEGYAn Integrated Emission Management strategy is designedthat schedules the control of the EGR valve position, theVTG position, and the urea dosing, as illustrated in Figure 3.Focus is on NOx reduction in this example.
SCR NOx conversion efficiency is determined by the exhaustgas flow, catalyst temperature, and the ammonia storage onthe SCR catalyst. The considered Cu-Zeolite SCR catalysthas very high ammonia storage capacity and ammoniaadsorption and desorption rates appear slow at relatively lowtemperatures. These catalyst characteristics imply that theshort term NOx conversion is hardly controllable by theinstantaneous urea injection rate. Consequently, the ureainjection rate is controlled to keep a certain level of ammoniastorage on the SCR catalyst. The SCR ammonia storage levelis controlled to be safe from excessive ammonia slip in caseof a sudden temperature increase. These ammonia storagecontrol strategies have proven very successful for SCRcatalysts with significant ammonia storage [10]. As the ureainjection rate can not control short term NOx conversion,instantaneous tailpipe NOx emission is controlled by theengine through engine-out NOx rates, exhaust gas flow andtemperature. Note that this paradigm is different from EuroIV/V-like systems for which tailpipe NOx emission iscontrolled by adjusting the urea injection rate for a Vanadiumbased SCR system.
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Figure 3. Structure of proposed Integrated EmissionManagement strategy that coordinates the control ofEGR position (uEGR), VTG position (uVTG) and urea
dosing (̇murea)
Optimal ControlThe control challenge is concentrated on engine airmanagement: control EGR valve and VTG position to meetengine out emission constraints while minimizing operationalcosts associated with fuel and urea consumption. Adoptingthe proposed Integrated Emission Management philosophy,this control problem has to be solved in an optimal sense.Optimal control theory offers a structured and soundapproach for these problems. Mathematical derivation isintricate, but the structure of the optimal control solution hasan intuitive interpretation. All boils down to optimizing anextended cost function, called the Hamiltonian [11]:
(1)
A derivation of this optimal control structure is presented inthe Appendix. Given the engine operating point andaftertreatment temperatures, the Hamiltonian is onlydependent on the VTG and EGR valve setpoints. TheHamiltonian, which has unit [€/s], has to be minimized ateach time instance. The first term in the Hamiltonianrepresents the contribution of the (current) operatingcondition to the total operating costs of the application. The
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additional terms stem from the fact that not only operatingcosts have to be minimized, but also a constraint on NOxemission has to be met. They represent equivalent pricesassociated with raw NOx emission and temperature changes.The three lambdas have the following interpretation:
• λ1 [€/°C]: equivalent cost associated with a 1 °Ctemperature drop of the DOC/DPF combination within 1 s, oralternatively: −λ1 is the value associated with a DOC/DPFtemperature raise of 1 °C within 1 s;
• λ2 [€/°C]: equivalent cost associated with a 1 °Ctemperature drop of the SCR catalyst within 1 s, oralternatively: −λ2 is the value associated with an SCRtemperature raise of 1 °C within 1 s;
• λ3 [€/g]: equivalent cost associated with 1 g of engine-outNOx emission.
The structure of the proposed control strategy is depicted inFigure 3. The urea dosing control is separate from the rest ofthe control strategy as it controls the SCR NH3 storageindependently. A well performing SCR NH3 storagecalibration is mainly dependent on the SCR temperature;direct dependency on engine-out conditions (i.e. raw NOxemissions) is weak and therefore neglected. The low levelSCR control controls the urea dosing to track the NH3 storagesetpoint. An on-line SCR NH3 storage model is assumed tobe available for this purpose.
The main task for the Integrated Emission Management layeris minimization of the Hamiltonian. The Hamiltonian isconstructed online from several engine and aftertreatmentmaps that describe trade-offs between control variables, onone hand, and operating costs and emissions, on the otherhand. All maps are generated from the one dimensionalaftertreatment models and the mean-value engine modelthrough an automated procedure. For the currentimplementation, instantaneous minimization of theHamiltonian is performed by a gradient descent procedure.Minimization of the Hamiltonian yields setpoint values forEGR valve and VTG positions (uff
EGR and uffVTG). These are
passed as feedforward signals to the low level EGR/VTGcontroller together with two associated performanceparameters: fresh air flow and O2 concentration in intakemanifold. These performance parameters are closely relatedto the level of engine-out NOx and PM emissions. The lowlevel EGR/VTG controller's task is to track theseperformance parameters robustly in case of system deviationsand to facilitate dynamic control.
Apart from several maps, the Hamiltonian is also constructedfrom estimates of anticipated aftertreatment temperaturechanges as a result of VTG and EGR setpoints. A simplifiedthermal model is used to describe these anticipated
temperature changes. The simplified model is described inthe Appendix. The anticipated DOC and SCR temperaturechange rates can be calculated given EGR and VTG setpointsand the maps for engine-out temperature and exhaust massflows as a function of engine operating point.
Selection of Control Parameters λ1 to λ3
Given all information to construct the Hamiltonian, the resultof the minimization in terms of VTG and EGR setpoints isfully determined by the equivalent price parameters λ1 to λ3.As an example, trade-offs are studied for one arbitrarystationary engine operating point: 1400 rpm, 50% torque(1174 Nm). Fixed prices for diesel fuel and urea solution aswere used in this study are listed in Table 1. The prices arebased on European currency and price level. The costassociated with soot reduction through active DPFregeneration is estimated based on an assumed soot loadregeneration trigger level of 5 g/l and the energy requirementfor a typical active regeneration in which the DPF is operatedat 600 °C for 10 minutes.
Table 1. Prices
Table 2 shows different BSFC-NOx trade-offs for differentvalues for the equivalent price parameters λ1 to λ3. As theequivalent price on engine-out NOx emission (λ3) isincreased, the engine-out NOx emission drops at an expenseof an increased fuel consumption. Lower engine-out NOxemissions are realized by increasing the EGR rate throughclosing the VTG. An EGR actuator setpoint of 90%represents the maximum EGR valve opening for this engineoperating point. The post-turbine exhaust gas temperature canbe influenced through λ1. For this example, the temperatureincrease is realized through fully opening the VTG. Underdifferent circumstances a temperature raise is accomplishedby further closing the VTG. This is for example the case atsituation with more EGR and at low load conditions (e.g.idle). It is maybe striking that λ2 does not affect the engine-out exhaust gas temperature. This can be explained by thefact that λ2 puts a price on heat convection from the DOC/DPF to the SCR substrate. This convection is often better offwith a decent exhaust gas flow and does not directly benefitfrom a rise of exhaust gas temperature for instance throughchocking the engine. Obviously the SCR heat up is affectedindirectly via the DOC-SCR temperature difference.
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Table 2. Demonstration of different trade-offs for 1400rpm, 50% torque operating point
1uvtg = 0% corresponds to VTG in closed position2uegr = 0% corresponds to closed EGR valve
Minimizing the Hamiltonian is only part of a real optimalcontrol solution. In order to find the real optimum, thetrajectories of the equivalent price parameters λ1(t) to λ3(t)have to be optimal too. For the problem considered λ3 isconstant (see Appendix), but λ1 and λ2 are time-variant.Determination of the exact optimal course of the equivalentprice parameters λ can be a tedious job [12]. Furthermore, theresulting control strategy may only be fit for the particulartest cycle it was designed for. A reliable and general rule forthe equivalent price parameters is desired instead forimplementation in a realistic controller.
A pragmatic approach is adopted in this work: the course oftemperature related equivalent price parameters λ1 and λ2 willbe described by a heuristic, postulated rule. It seems fair thatthe effort taken to heat up the aftertreatment is proportional toSCR catalyst NOx conversion inefficiency. When the DOC/DPF temperature is lower or marginally higher than the SCRsubstrate temperature, it seems better to invest in raising theengine-out exhaust temperature rather than promoting heatconvection from DOC/DPF to SCR. The converse holdswhen the DOC/DPF temperature is significantly higher thanthe SCR. Bringing these observations together results in rulesfor λ1 and λ2 as depicted in Figure 4.
Figure 4. Rule for λ1 and λ2
The proposed rule is parameterized by λT, ΔT1 and ΔT2.These parameters together with constant λ3 are optimizedsuch that the first term in Eq. 1 representing the expectedoperational costs is minimized over a hot WHTC test cycle,while the weighted cold/hot NOx emission stays comfortablybelow the applicable emission limit.
BASELINE STRATEGYFor comparison, a baseline strategy is calibrated in a way thatresembles common practices in the development of an enginecalibration. The baseline strategy is assumed to be state-of-the-art in a sense that it can switch between different modeswith different engine calibrations. In this example, two modesare considered:
1. thermal management mode for promoting emissionreduction by exhaust gas aftertreatment through rapid heat-up, and;
2. a ‘normal low NOx mode.
Typically an estimate of the achievable SCR NOx conversionon the applicable test cycle is known beforehand from ademonstrator or pre-development program. Using thisinformation, emission targets are set for the raw emissions. Apossible Euro VI emission budget is presented in Table 3. Forthis scenario, a weighted cold/hot WHTC cycle NOx limit of0.46 g/kWh is assumed with cold and hot cycle weights of16% and 84% respectively.
Table 3. Euro VI NOx emission targets for baselinestrategy
For an assumed SCR NOx conversion efficiency of 80% and90% for the cold and hot cycle respectively, an engine-outNOx emission level of 3.5 g/kWh will result in tailpipeemissions meeting the engineering target which is taken 10%below the WHTC NOx emission limit. This 3.5 g/kWh levelhas to result from a combination of thermal managementmode and the normal low NOx mode. The thermalmanagement mode is assumed to be triggered whenever theSCR catalyst temperature is below 200 °C. Once active,thermal management mode will remain active until the SCRcatalyst has reached 250 °C. It will be active in both the coldas the hot part of the WHTC. The hot cycle starts with thelow load urban part of the WHTC right after the soak period.Thermal management is active in this part of the test to keep
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the aftertreatment at a preferable temperature. Thermalmanagement mode will be active longer in the cold startcycle. It is assumed that on average the thermal managementmode is active for 30% of test cycle time. In order to allowsome freedom for thermal management mode to favoraftertreatment heat-up, the requirement on engine-out NOxemission is relaxed. This results in a more stringentrequirement on engine-out NOx emissions for the normalmode. However, a too low target for the normal mode willresult in a undesired fuel penalty. Therefore, the engine-outNOx emission target for normal mode is set to 3.0 g/kWh.This leaves a NOx limit of 4.7 g/kWh for the thermalmanagement mode.
In practice, the engine calibration will be optimized to reduceoperation costs (fuel and urea consumption) for expected dutycycle of the engine. In contrary to the Integrated EmissionManagement approach presented in this paper, the actualstate (temperature) of the aftertreatment system is not fullytaken into account. The calibration is likely to be optimizedassuming steady-state temperatures for the aftertreatmentsystem. This fits into the presented optimal controlframework through assuming steady-state temperatures forTDOC and TSCR. That is, the DOC and SCR efficiency mapsin the Hamiltonian are evaluated using the anticipated steady-state DOC and SCR temperatures that would result for eachcombination of VTG and EGR valve setpoints. In this way,the same control structure can be used to compare thebaseline strategy to the Integrated Emission Managementstrategy. Two sets of constant equivalent price parameters λ1to λ3 are used in the baseline strategy: one for normal modeand one for thermal management mode. For the normal lowNOx mode λ1 and λ2 are zero while λ3 is tuned to accomplish3.0 g/kWh engine-out NOx emission on the WHTC. Forthermal management mode λ3 is kept the same. λ1 is tuned toget maximal exhaust gas temperature increase while the rawNOx emissions approach 4.7 g/kWh.
SIMULATION RESULTSThe Integrated Emission Management strategy is comparedto the baseline strategy through simulation of the Euro VIheavy-duty engine plus aftertreatment configuration. For thepresented results, the engine is assumed to be representeddirectly by the stationary maps derived from the mean-valueengine model. The aftertreatment is simulated by the full onedimensional first principles models.
Both the Integrated Emission Management strategy and thebaseline strategy are optimized as described in previoussections for the engine operating point distribution visualized
in Figure 5. This distribution represents a long-haulapplication duty cycle. The figure shows a contour plot forthe weights w in Eq. 1. Prices given in Table 1 are used for allsimulations.
Figure 5. Engine operating point distribution for long-haul application
The baseline strategy is calibrated as discussed in theprevious section. This has led to the following values for theequivalent price parameters: λ3 = 0.0575 €/g, λ2 = 0 €/°C forboth modes. For thermal management mode, λ1 =−1.298·10−4 €/°C, whereas λ1 = 0 €/°C is applied for normalmode. Figure 6 shows a comparison for the thermalmanagement mode and the normal low NOx mode on the coldstart WHTC test cycle. The average SCR catalyst temperatureis 18 °C higher for the thermal management mode. Engine-out NOx emission on this cold WHTC is 4.86 g/kWh for thethermal management mode and 2.99 g/kWh for the normallow NOx mode. When referred to the ‘baseline’ strategy inthe presentation of the simulation results, mode switching isassumed such that the engine switches from thermalmanagement to low NOx mode when the SCR catalyst hasreached 250 °C. The cold WHTC NOx cycle result is 0.78 g/kWh for this baseline strategy. In this case, engine-out NOxemissions are 3.73 g/kWh and peak tailpipe ammonia slip is 8ppm.
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Figure 6. Comparison of thermal management mode andnormal mode for baseline strategy on cold WHTC
Optimization of the Integrated Emission Managementstrategy has resulted in the following parameter settings: λT =−1.173·10−5 €/°C, λ3 = 0.0082 €/g, ΔT1 = 16 °C and ΔT2 =51 °C. Figure 7 shows a comparison of the IntegratedEmission Management strategy and the baseline strategy. TheIntegrated Emission Management strategy does not invest inraising the temperature of the exhaust gas aftertreatment.Studying the course of the cumulative NOx emissions overthe test reveals some significant differences between the twostrategies. An important difference is made right at the startof the test. The Integrated Emission Management strategytries to keep engine-out NOx emissions as low as possible byapplying EGR. This pays off at the end of the cycle. The lastquarter of the cycle corresponds to highway drivingconditions [13]. As the calibration is optimized for long-hauland highway applications, the Integrated EmissionsManagement strategy tries to operate the engine for theseconditions as efficiently as possible. This results in higherengine-out NOx emissions. The baseline strategy is alsooptimized for the same duty cycle, but it does not offer thesame degree of flexibility to raise engine-out NOx emissionswhen conditions are beneficial for NOx reduction byaftertreatment. Tailpipe NOx levels are similar for bothstrategies. Cold and hot WHTC cycle results for bothstrategies are summarized in Table 4.
Figure 7. Comparison of Integrated EmissionManagement strategy an baseline strategy on hot WHTC
Table 4. WHTC summary for baseline and IntegratedEmission Management strategy
1predicted fuel consumption relative to baseline strategy
Table 4 shows that the engine-out NOx emission for theIntegrated Emission Management strategy are higher than forthe baseline strategy. The predicted improvement on fuelconsumption is 2%. It is expected that the improved fueleconomy is partially due to the higher raw NOx emission, butalso due to the fact that the Integrated Emission Managementstrategy realizes the engine-out NOx level more effectively.The baseline strategy realizes the 3.73 or 3.46 g/kWh engine-out NOx emission level by switching form thermalmanagement to low NOx mode. Both modes are suboptimalin terms of fuel economy: one mode sacrifices fuel economyfor higher exhaust gas temperatures, while the other sacrificesfuel economy for lower engine-out NOx emissions.
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The difference between the two strategies is even moreprominent when they are compared on the duty cycle, whichthey were optimized for: highway driving. Table 5 shows theresult for a simulation where the two strategies are comparedbased on a 1 hour test cycle which is compiled fromrepetitions of the highway part of the WHTC. Both strategiessatisfy in-use NOx emission requirements: the baselinestrategy demonstrates 0.180 g/kWh tailpipe NOx while theIntegrated Emission Management strategy shows 0.254 g/kWh NOx emissions. Tailpipe ammonia slip is negligible forboth strategies.
The estimated fuel consumption benefit from the IntegratedEmission Management strategy is 3% for this highway cycle.The Integrated Emission Management strategy saves fuel bycutting back on the EGR rate. As a result, engine-out NOxemissions and urea consumption are higher. Based on theprices from Table 1, the benefit in fluid costs (fuel + urea) isestimated 2.5%. The reduced EGR rate has some furtherbenefits. The heat rejection of the engine will be lower whichcan reduce the power demand for the radiator fan.Furthermore engine-out soot emission and DPF regenerationfrequency will be lower. The reduction of soot emissionsfrom the engine due to lower EGR rates is estimated to be30% but needs further validation.
It is clear that the Integrated Emission Management strategyallows additional freedom to optimize the engine operationgiven the state of the exhaust gas aftertreatement. Theproposed strategy fully exploits the additional degrees offreedom to optimize operating costs for the relevant dutycycle. The presented baseline strategy offers less flexibilityand, therefore, has to compromise on efficiency. Withouttaking the state information of the aftertreatment intoaccount, a strategy can not differentiate between situationswhere the same engine operating point is ran with a hot orcold exhaust gas aftertreatment system.
FUTURE WORK ON CONTROLSTRATEGYIn order to further validate the performance of the presentedcontrol strategy, a study has to be done using the full mean-value emission model and eventually the strategy has to betested on a real engine setup. Prior to these activities,
dynamic performance has to be addressed. Furthermore, it isplanned to extend the control strategy with one additionaldegree of freedom: injection timing. In order to reduce thedependency on large empirical datasets used for predictionengine emission performance, a phenomenological emissionmodel as presented in [14] can be used instead. The presentedIntegrated Emission Strategy exploits all freedom to realizelow NOx emissions and low fuel consumption. For practicalapplication, the framework needs to be extended withperformance, driveability and durability constraints.
GENERAL IEM ARCHITECTUREAfter presenting the Integrated Emission Managementapproach and an applied example, this section discusses theextension of the control strategy to a general controlarchitecture. The proposed control architecture is shown inFigure 9. Three main parts can be distinguished:
• State information: this layer gathers all informationrequired for decision making and optimization by thesupervisory IEM controller from real sensors as well asvirtual sensing. Consequently, unprecedented informationbecomes available, such as DPF soot load, SCR ammoniasurface coverage, and energy flows;
• Supervisory controller: based on the subsystem stateinformation and the requirements set by the driver's torquerequest and by emission legislation, optimal control settingsare determined on-line. This layer encompasses allinformation on the interactions between engine andaftertreatment system and coordinates the control of thesubsystems. Setpoints are passed to the low level controllers.On-Board Diagnostics is integral part of the control strategy;
• Low-level controllers: these subsystem and componentcontrollers aim to realize the desired set-points in a robustway.
The proposed structure allows partitioning of control subfunctions and serves as a plan for designing powertraincontrols with increasing complexity.
OUTLOOKThe next challenge ahead for the industry is the reduction ofCO2 emissions from road transport [1]. This emphasizes theneed for a system approach and also boosts research intoadvanced thermal management concepts, hybrid drivetrains,and energy recovery systems. To deal with this furtherincrease in complexity and to meet future emission and fuelconsumption targets simultaneously, the ultimate goal inoptimal powertrain control is an integrated energy andemission management strategy [11, 15]. This is seen as anevolution of the presented Integrated Emission Managementstrategy. This generic and systematic approach can be easilyextended to deal with the requirements of the energymanagement strategy; the objective function in terms of
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operational costs also holds for future powertrains. Additionalconstraints have to be added, e.g. for battery charge.
As powertrain complexity will continue to expand, it isenvisioned that application of model-based and optimalcontrol techniques will gradually become more intense.Model-based control is already applied in areas were detailson interaction between sub functions is hard to fullycomprehend: e.g. coordinated EGR/VTG control [16]. Withthe further increase of both complexity and performancedemands, control development, and especially calibration,becomes a daunting task; the degree of system interactionwill go beyond the grasp of human's intuition. In the near
future, control design and calibration processes will beintensively supported by mathematical and model-basedtools, which will lead to an increased level of automation.
Another dimension in the expected evolution of futurepowertrain control is the transition from off-line towards on-line optimization, as illustrated in Figure 8. The presentedIntegrated Emission strategy is already an example of thisdevelopment: the air management is adapted on-line using theactual state of the aftertreatment. On-line optimization is alsobeneficial with respect to upcoming requirements for real-world operation (In-Use Compliance); the powertrainrobustness is enhanced, since the proposed control strategy
Figure 9. General Integrated Emission Management control architecture
Figure 8. Future evolution of powertrain control
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deals with the varying operating conditions that are faced onthe road. When route information (E-horizon) becomesavailable in the near future, on-road system performance caneven be optimized over expected duty cycles for a specifictime horizon.
CONCLUSIONSAn Integrated Emission Management (IEM) concept has beenpresented that serves as a generic framework for emissioncontrol and future powertrain control development. Thepotential of this concept is demonstrated for a Euro VI long-haul truck application. From the results presented in thiswork, the following conclusions are drawn:
• Integrated Emission Management is a model-based controldesign philosophy that minimizes fuel consumption, whilemeeting emission constraints through optimal exploitation ofsystem interactions. This structured approach explicitly dealswith emission constraints and operating costs.
• Following the Integrated Emission Management approach, acost-optimal control strategy is designed for optimal airmanagement and aftertreatment interaction for a Euro VIlong-haul truck application. Using optimal controltechniques, this strategy focuses on NOx reduction by on-lineadjusting EGR/VTG control settings based on the actual SCRNOx reduction capacity.
• The proposed cost-optimal control strategy accomplishes2% to 3% reduction in fuel consumption and total fluid costscompared to a baseline strategy in a simulation study. Thenew strategy is capable of this efficiency improvementmainly because it is able to differentiate between situationswhere the same engine operating point is ran with a hot orcold exhaust gas aftertreatment system. Further potentialbenefits include reduced heat rejection, reduced DPFregeneration frequency, and reduction of the calibrationcomplexity as the complete EGR/VTG setpoints are alteredthrough adjustment of just three equivalent price parameters.
• Integrated Emission Management is a generic andsystematic approach that can easily be extended towardsintegrated energy and emission management to meet therequirements for future powertrains; besides targets for theemission of pollutants, CO2 emission limits have to be dealtwith.
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7. Dekker, H. J. and Sturm, W.L., “Simulation and Controlof a HD Diesel Engine Equipped with New EGRTechnology,” SAE Technical Paper 960871, 1996, doi:10.4271/960871.
8. Seykens, X.L.J., Baert, R.S.G., Willems, F.P.T., Vink, W.and van denHeuvel, I.T.M., “Development of a dynamicengine brake model for control purposes”, presented at NewTrends in Engine Control, Simulation and Modelling, France,2-4 October, 2006.
9. Cloudt, R., Saenen, J., van den Eijnden, E., and Rojer, C.,“Virtual Exhaust Line for Model-based Diesel AftertreatmentDevelopment,” SAE Technical Paper 2010-01-0888, 2010,doi:10.4271/2010-01-0888.
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11. Kessels, J., Willems, F., Schoot, W. and van den Bosch,P., “Integrated Energy & Emission Management for HybridElectric Truck with SCR aftertreatment”, presented at IEEEVehicle Power and Propulsion Conference (VPPC), France,September 1-3, 2010.
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15. Willems, F. and Foster, D., “Integrated powertraincontrol to meet future CO2 and Euro-6 emissions targets for adiesel hybrid with SCR-deNOx system”, presented at 2009American Control Conference, USA, June 10-12, 2009.
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International Symposium on Advanced Vehicle Control(AVEC), August 22-26, 2010.
17. Geering, H.P., “Optimal Control with EngineeringApplications”, Springer, Berlin Heidelberg, ISBN978-3-540-69437-3.
OPTIMAL CONTROL SOLUTIONProblem DefinitionOptimal control theory [17] is applied to the problem ofcontrol design for optimal air management and aftertreatmentinteraction. Definition of an optimal control problemcomprises four aspects:
1. cost function;
2. constraints;
3. trajectory;
4. system description.
Cost FunctionFor the considered powertrain control problem operatingcosts have to be minimized. Operating costs are constitutedby fuel cost, urea cost and cost associated with net PMemission through fuel required for active DPF regeneration:
(A1)
ConstraintsA NOx emission limit forms a constraint for the optimalcontrol problem: at the end of a test cycle the cumulativeNOx emission can not exceed a set target.
TrajectoryThe definition of the optimal control problem is incompletewithout a specification of the trajectory over which the costfunction has to be minimized and for which the constraintshave to be met. For the considered problem, emissionconstraints have to be met over the WHTC test cycle.Operating costs do not necessarily have to be minimized overa test cycle but rather on a relevant duty cycle. The weight wis included in Equation A1 to assure that when F isminimized over a WHTC, the engine operating pointsrelevant to the considered duty cycle are assigned moreweight.
System DescriptionThe system description defines the dynamical system theoptimal control problem is solved for. Possibly the dynamicalsystem is augmented with additional states to impose desiredconstraints as constraints on the states of the augmenteddynamical system. The dynamics relevant to the consideredproblem stem from the thermal behavior of the exhaust gasaftertreatment system. A simplified thermal model is used inthe system description for the optimal control problem:
(A2)
(A3)
where cp,g is specific heat capacity of exhaust gas, CDOC isthe total heat capacity of the DOC, CSCR is about half of thetotal heat capacity of the SCR catalyst, h is the ambient heattransfer coefficient, Tamb is the ambient temperature and Teois the engine-out exhaust gas temperature. Eq. A2 isrepresentative for the rate of temperature change of the DOCand DPF combined; their mean temperatures areapproximated as equal. TDOC should therefore be a measurefor the mean DOC and DPF temperature and is chosen as theactual post-DOC temperature. Eq. A3 represents the rate oftemperature change of the SCR due to convection from theDOC/DPF combination while TSCR is the mean actual SCRtemperature. CDOC, CSCR and h are tuning parameters andcan be used to approximate the temperature of the moredetailed virtual exhaust line model. Table A1 defines thevalues as they were found for the considered aftertreatmentsystem. The fit of this simplified thermal model can bejudged from Figure A1.
Table A1. Parameters simplified thermal model
Figure A1. Simplified thermal model fit for hot WHTC
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Equations A1 and A2 are written in a state-space formaugmented with a state representing the cumulative tailpipeNOx emission:
(A4)
with and .
SolutionThe solution to the optimal control problem described aboveis obtained through application of Pontryagin's MinimumPrinciple to the Hamiltonian [17]:
(A5)
(A6)
(A7)
Optimal control output u* is obtained from Eq. A6 throughfinding the minimum of the Hamiltonian (Eq. A5). Theevolution of the controller states λ is dictated by Eq. A7.Finding the optimal solution which satisfies a constraint onthe cumulative NOx emission mNOx,eo over a WHTC testcycle is now reduced to finding the suitable set of initialcontroller states λ. This in itself can be challenging as a Two-Point Boundary Value Problem has to be solved while theinvolved differential equation for the controller states istypically unstable due to the finite horizon of the optimizationproblem [12]. Moreover, the obtained control strategy mightonly be fit for the particular trajectory it was designed for. Apragmatic suboptimal approach using heuristic rules for λ hasbeen adopted in this work instead. The structure of theoptimal control solution is maintained however: optimalcontrol outputs are obtained through minimization of theHamiltonian.
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