Reduction of transient soot emissions of a production Diesel engine using a fast soot sensor and closed loop control

9th 9th IFAC IFAC International International Symposium Symposium on on Advances Advances in in Automotive Automotive Control 9th IFAC International Symposium on Advances in Automotive Available online at www.sciencedirect.com Control 9th IFAC France, International Orléans, June Symposium 23-27, 2019 on Advances in Automotive Control Orléans, France, June 23-27, 2019 Control Orléans, France, June 23-27, 2019 Orléans, France, June 23-27, 2019

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IFAC PapersOnLine 52-5 (2019) 159–164

Reduction Reduction of of transient transient soot soot emissions emissions of of a a Reduction of transient soot emissions of a production Diesel engine using a fast soot sensor Reduction of transient soot emissions of a production Diesel engine using a fast soot sensor production Diesel engine using a fast soot sensor and loop control production Diesel engine using a fast soot sensor and closed closed loop control and closed loop control and closed loop control Florian Meier ∗∗ Patrick Schrangl ∗∗ Luigi del Re ∗∗

Florian Meier ∗∗ Patrick Schrangl ∗∗ Luigi del Re ∗∗ Florian Meier ∗ Patrick Schrangl ∗ Luigi del Re ∗ Florian Meier Patrick Schrangl Luigi del Re ∗∗ Institute Institute for for Design Design and and Control Control of of Mechatronical Mechatronical Systems Systems ∗ InstituteJohannes Kepler University Linz, Austria for Design and Control of Mechatronical Systems ∗ InstituteJohannes Kepler University Linz, Austria for Design and Control of Mechatronical Systems (e-mail: [email protected], [email protected], [email protected]) Johannes Kepler University Linz, Austria (e-mail: [email protected], [email protected], [email protected]) Johannes Kepler University Linz, Austria (e-mail: [email protected], [email protected], [email protected]) (e-mail: [email protected], [email protected], [email protected]) Abstract: Unfavorable Unfavorable combustion combustion conditions conditions during during engine engine transient transient operation operation cause cause significantly significantly Abstract: higher emissions emissions of toxic toxiccombustion pollutants in in comparison to static static conditions. Aoperation closed loop loop controller could Abstract: Unfavorable conditions during engine transientA cause significantly higher of pollutants comparison to conditions. closed controller could Abstract: Unfavorable combustion conditions during engine transient operation cause significantly help to reduce these emissions under real driving conditions, provided the necessary measurements are higher emissions of toxic pollutants in comparison to static conditions. A closed loop controller could help to emissions reduce these emissions underinreal driving conditions, provided the measurements are higher of toxic pollutants comparison to static conditions. A necessary closed loop controller could available, in the diesel case mainly soot and NOx. help to reduce these emissions under real driving conditions, provided the necessary measurements are available, in thethese diesel case mainly soot and NOx. conditions, provided the necessary measurements are help to paper reduce emissions under real driving available, in the case soot and NOx. of In this this we diesel analyze themainly potential improvement of raw raw soot soot emissions emissions using using fast fast measurements. measurements. To To In paper we analyze the potential improvement available, in the diesel case mainly soot and NOx. this end, we consider the cumulated emissions obtained with the standardusing ECUfast setting and both both with with In this paper we analyze the potential improvement of rawwith sootthe emissions measurements. To this end, we consider the cumulated emissions obtained standard ECU setting and In this paper we analyze potentialemissions improvement of and raw asoot emissions using measurements. To a new, new, fast Laser Induced Incandescence (LII) sensor sensor production opacimeter. First, an emission this end, weLaser consider thethe cumulated obtained the standard ECUfast setting and with athis fast Induced Incandescence (LII) andwith a production opacimeter. First, anboth emission end, we consider the cumulated emissions obtained with the standard ECU setting and both with model based on fast fast dynamic soot measurements measurements is used used to aestimate estimate the ideal ideal tradeoffFirst, between soot and and amodel new, based fast Laser Induced Incandescence (LII) sensor and production opacimeter. an emission on dynamic soot is to the tradeoff between soot amodel new,Then fast Laser Induced Incandescence sensor and production opacimeter. an emission based fast dynamic sootacting measurements ispressure used to ais estimate ideal tradeoff between soot and NOx. a on feedback controller acting on(LII) the rail rail pressure is used to tothe approximate theFirst, theoretical target. NOx. Then a feedback controller on the used approximate the theoretical target. model baseda on fast dynamic sootacting measurements used estimate ideal between and Experimental results confirm that high on reduction in soottocan can be achieved withtradeoff very low price soot intarget. terms NOx. Then feedback controller the railisin pressure is used tothe approximate the low theoretical Experimental results confirm that aa high reduction soot be achieved with aa very price in terms NOx. Then a feedback controller acting on the rail pressure is used to approximate the theoretical target. of NOx NOx (-50%/+5%). (-50%/+5%). Experimental results confirm that a high reduction in soot can be achieved with a very low price in terms of Experimental results confirm that a high reduction in soot can be achieved with a very low price in terms of NOx (-50%/+5%). © NOx 2019, (-50%/+5%). IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. of Keywords: Emissions, Emissions, Soot Soot Reduction, Reduction, Optimal Optimal Control, Control, Closed Closed Loop Loop Control Control Keywords: Keywords: Emissions, Soot Reduction, Optimal Control, Closed Loop Control Keywords: Emissions, Soot Reduction, Optimal Control, Closed Loop Control 1. INTRODUCTION INTRODUCTION as as Ortner Ortner and and Del Del Re Re (2007) (2007) or or Li Li et et al. al. (2017) (2017) did. did. Another Another 1. Another promising method includes adapting post injections in order 1. INTRODUCTION as Ortner and Del Re (2007) or Li et al. (2017) did. promising method includes adapting post injections in order to to 1. INTRODUCTION as Ortner and Delsoot Re (2007) or as Li demonstrated etpost al. injections (2017) by did. Another reduce engine-out emissions Lind et al. promising method includes adapting in order to reduce engine-out soot emissions as demonstrated by Lind et al. methodsoot includes adapting post injections order to Transient emission emission reduction reduction of of combustion combustion engines engines has has been been promising reduce engine-out emissions as demonstrated byin Lind et al. (2018). Transient (2018).engine-out soot emissions as demonstrated by Lind et al. Transient emission reduction of combustion engines has been reduce an important research topic over the last decades as Hagena (2018). an important research topic over the last decades as Hagena Transient emission reduction ofHagena combustion engines been This et al. al. (2011), Johnson (2012), et al. al. (2006)ashas and Han (2018). an important research topic over the last decades Hagena This work work focuses focuses on on limiting limiting soot soot emissions emissions using using aa feedback feedback et (2011), Johnson (2012), Hagena et (2006) Han an important research topic over the last decades asand Hagena This work based focuses onfast limiting soot emissions using a feedback controller on measurements. As a control input al. (1996) show. et al. (2011), Johnson (2012), Hagena et al. (2006) and Han controller based on fast measurements. As a control input the the et al. (1996) show. This work focuses on limiting soot emissions using a feedback et al. (1996) (2011),show. Johnson (2012), Hagena et al. (2006) and Han controller chosen in order mixture rail pressure ppRail based on was fast measurements. Asto a improve control input the et al. rail pressure was chosen in order to improve mixture Rail controller based on fast measurements. As a control input the Although modern engines already achieve acceptable results, et al. (1996) show. engines already achieve acceptable results, rail formation for cleaner combustion. The rail pressure was was chosen in order improve mixture pressure Although modern formation for ppaaRail cleaner combustion. Theto pressure was was chosen in order to rail improve mixture pressure there is is demand demand forengines more sophisticated sophisticated strategies in order orderresults, to fulful- rail Although modern already achieve acceptable Rail chosen based on sensitivity analysis of numerous air path and formation for a cleaner combustion. The rail pressure was there for more strategies in to Although modern engines already achieve acceptable results, chosen based on sensitivity analysis of numerous air path and for on a variables. cleaner combustion. railof pressure fill future future legal targets targets concerning emission limits. The to newly there is demand for moreconcerning sophisticated strategies in order ful- formation chosen based sensitivity analysis of The numerous air path was and injection control The positive effects increased rail fill legal emission limits. The newly injection control variables. The positive effects of increased rail there is demand for more sophisticated strategies in order to fulfill future legal targets concerning emission limits. The newly chosen based on sensitivity analysis of numerous air path and introduced Worldwide Harmonized Light Duty Test Procedure and injection pressure have been examined for a long time e.g. injection control variables. The positive effects of increased rail introduced Worldwide Harmonized Light Duty Test The Procedure and injection pressure have been examined for a long time e.g. fill future legal targets concerning emission limits. newly control variables. The positive effects of increased rail (WLTP) with with an increased increased proportion of Duty transient maneuvers introduced Worldwide Harmonized Light Testmaneuvers Procedure injection by Rajalingam and Farrell (1999), Pickett and Siebers (2004) and injection pressure have been examined for a long time e.g. (WLTP) an proportion of transient introduced Worldwide Harmonized Light Duty Testmaneuvers Procedure by Rajalingam and Farrell (1999), Pickett and Siebers (2004) and injection pressure have been examined for a long time e.g. and the introduction of real driving emission (RDE) tests re(WLTP) with an increased proportion of transient or Rao et Rajalingam and Farrell (1999), Pickett and Siebers (2004) and the introduction of realproportion driving emission (RDE) tests re- by (WLTP) with an increased of transient maneuvers Rao et al. al. (2018). (2018). by Rajalingam and Farrell (1999), Pickett and Siebers (2004) quirethe emission control not only for standardized standardized cycles on the the and introduction ofnot realonly driving emission (RDE) tests re- or or Rao et al. (2018). quire emission control for cycles on and introduction realonly driving emission (RDE) re- or On the measurement quirethe emission not for standardized cyclestests on the Rao et al. (2018). side testbench but in incontrol everyof possible scenario on the the roads. roads. On the measurement side aa prototype prototype Laser Laser Induced Induced IncandesIncandestestbench but every possible scenario on quire emission not onlyscenario for standardized cycles on the On cence (LII) sensor was used. Its fast dynamics and response the measurement side a prototype Laser Induced Incandestestbench but incontrol every possible on the roads. cence (LII) sensor was used. Its fast dynamics and response the measurement sideused. a prototype Laser Induced IncandesUnfavorable conditions, such scenario as oxygen oxygen shortage due to to an an On testbench but conditions, in every possible on shortage the roads.due cence (LII) sensorprecise was Its fast dynamics and response time allow more dynamic measurements and models Unfavorable such as time allow more precise dynamic measurements and models Unfavorable conditions, such as oxygen shortage due to an cence (LII) sensor was used. Its fast dynamics and response abrupt increase increase of of the the injected injected fuel fuel mass mass after after aa gas gas pedal pedal tip-in, tip-in, time and therefore presumably a better performance of the closed allow more precise dynamic measurements and models abrupt Unfavorable conditions, such as oxygen shortage due to an and therefore presumably a better performance of the closed allow more precise dynamic measurements and models lead to to increase transientofemission emission peaks as mass examined by Bazari (1994), abrupt the injected fuel after by a gas pedal(1994), tip-in, time loop system. and therefore presumably a better performance of the closed lead transient peaks as examined Bazari abrupt increase of the injected fuel mass after a gas pedal tip-in, loop system. and therefore Selmanaj et al. al. emission (2014), Tan Tan et as al.examined (2014), Kang Kang and (1994), Farrell loop lead to transient peaks by Bazari system. presumably a better performance of the closed Selmanaj et (2014), et al. (2014), and Farrell lead to or transient peaks by Bazari discusses loop paper system. (2005) Hirsch et al. al. (2010). (2010). Selmanaj et al. emission (2014), Tan et as al.examined (2014), Kang and (1994), Farrell The paper discusses the the impact impact of of rail rail pressure pressure variations variations on on (2005) or Hirsch et Selmanaj et al. (2014), Tan et al. (2014), Kang and Farrell The transient soot emissions and contains three main aspects: The paper discusses the impact of rail pressure variations on (2005) or Hirsch et al. (2010). transient soot emissions and contains three main aspects: The papersoot discusses theand impact of rail pressure variations on The inhomogeneous inhomogeneous mixture distribution throughout throughout the the comcom- transient (2005) or Hirsch et al.mixture (2010).distribution emissions contains three main aspects: The •• demonstrating the soot reduction potential when using The inhomogeneous mixture distribution throughout the comtransient soot emissions and contains three main aspects: bustion chamber causes local rich zones in which the fuel candemonstrating the soot reduction potential when using an an bustion chamber causes local distribution rich zones inthroughout which the the fuelcomcanThe inhomogeneous mixture optimal input trajectory from an offline optimization • demonstrating the soot reduction potential when using an bustion chamber causes local rich zones in which the fuel cannot be burnt completely and as a result high soot concentrations optimal input trajectory from an offline optimization not be burnt completely and as a result high soot concentrations • optimal demonstrating the soot reduction potential when using an bustion chamber causes localasrich zones inuse which the fuel caninput trajectory from an offline optimization are be produced. While production engines smoke limiters, not burnt completely and a result high sootsmoke concentrations are produced. While production engines use limiters, optimal input trajectory from antime offline optimization not be burnt completely and as a result high soot concentrations • exploiting this potential in real by using which reduce injected injected fuel mass massengines depending on available air are produced. While production use on smoke limiters, • exploiting this potential in real time by using aa feedback feedback which reduce fuel depending available air are produced. While production engines use smoke limiters, controller which adjusts the rail pressure in dependence of • exploiting this potential in real time by using a feedback which reduce injected fuel mass depending on available air to limit transient soot peaks at the cost of reduced torque controller which adjusts the rail pressure in dependence of to limitreduce transient soot fuel peaksmass at the cost of on reduced torque • exploiting thismeasured potential in real time by using a feedback which injected depending available air the currently soot concentration controller which adjusts the rail pressure in dependence of to limit transient soot peaks at the cost of reduced torque dynamics, academic research concentrates on improving the the currently measured soot concentration dynamics, academic research concentrates on improving the controller which adjustssoot the rail pressure in dependence of to limit transient soot peaks concentrates at the cost of reduced torque the currently measured concentration combustion conditions. dynamics, academic research on improving the combustion conditions. currently measured soot fast concentration dynamics, research concentrates on improving the •• the analyzing combustionacademic conditions. analyzing the the impact impact of of the the fast LII LII sensor, sensor, comparing comparing the the There are numerous different approaches including faster trackcombustion conditions. results using the LII versus using the standard sensor for • analyzing the impact of the fast LII sensor, comparing the There are numerous different approaches including faster trackresults using the LII versus using the standard sensor for • analyzing the the impact of the fast LII sensor, comparing the ing of of are the numerous airpath control control likeapproaches Alberer and andincluding del Re Re (2009), (2009), using There different faster trackfeedback control to answer the question whether the LII results using LII versus using the standard sensor for ing the airpath like Alberer del using There are numerous different approaches including faster trackfeedback control to answer the question whether the LII results using thebenefits LII versus using the standard sensor for early-injection like Liu et etlike al. Alberer (2015) or or for example by using ing of the airpath control andfor delexample Re (2009), using feedback control to answer the question whether the LII sensor provides for this application or not early-injection like Liu al. (2015) by sensor provides benefits for this application or not ing of the airpath control like Alberer and del Re (2009), using early-injection like Liu et al. (2015) or for example by using feedback control to answer the question whether the LII Model Predictive Control (MPC) for the airpath control such sensor provides benefits for this application or not Model Predictive for or the airpath control such early-injection likeControl Liu et (MPC) al. (2015) example by using sensor provides benefits for this application or not Model Predictive Control (MPC) for thefor airpath control such Model Predictive Control (MPC) for the airpath control 2405-8963 © 2019, IFAC (International Federation of Automatic such Control) Hosting by Elsevier Ltd. All rights reserved.

Copyright © 2019 IFAC 159 Copyright © under 2019 IFAC 159 Control. Peer review responsibility of International Federation of Automatic Copyright © 2019 IFAC 159 10.1016/j.ifacol.2019.09.026 Copyright © 2019 IFAC 159

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In Section 2, the engine, the test bench as well as the LII sensor are described briefly. In Section 3 the problem is stated explicitly to define the optimization goal. The choice of the control variable is explained as well as the potential analysis using an offline best-case optimization together with some measurement results. To enable online reductions a real-time capable feedback controller is presented in Section 4. In Section 5 the achieved soot reductions are demonstrated and the performance using different sensor signals as a feedback input are compared. 2. EXPERIMENTAL SETUP The considered engine is a BMW 4-cylinder Diesel engine with 2l displacement, common rail injection, variable turbine geometry (VGT) turbocharger and cooled exhaust gas recirculation (EGR). The engine is operated on the Johannes Kepler University’s light duty engine test bench, which is equipped with state-of-the-art measurement devices such as the AVL Micro Soot Sensor, AVL Opacimeter or the Continental Smart NOx Sensor to analyze the concentration of toxic pollutants such as soot or nitric oxids (NOx) in the exhaust gas. Additionally a LII prototype sensor is used, which uses the principle of laser induced incandescence. Therefore soot particles in the exhaust stream are heated by a pulsed laser beam in order to record the emitted light intensity of the luminous particles. The maximum measured light intensity is assumed proportional to the current soot concentration. A scheme of the sensor setup as well as a picture of the actual sensor head are depicted in Fig. 1. Through the in-situ measurement principle without diluting or preconditioning the measured exhaust gas more dynamic measurements are possible in comparison to aforementioned production type devices. This is especially useful when analyzing transient emission peaks occurring due to rapid changes in gas pedal position or engine speed. A more detailed description of the sensor’s properties, underlying physical principals, models, comparison with other measuring devices and a performance evaluation can be found in Zhang (2017).

• AVL Micro Soot Sensor: Photo acoustic device for mainly stationary measurements, high accuracy, strongly filtered signals, time delay > 1s 3. POTENTIAL ANALYSIS USING OFFLINE OPTIMIZATION 3.1 Optimization goal Optimal control is used to estimate the possible emission reduction potential. Minimizing cost function (1) provides input sequences to determine a maximum soot reduction potential for the chosen control variable independently of the controller’s performance for a given test cycle neglecting all modeling errors and external influences. As errors are inevitable, the results might not be the theoretical optimum but provide a suitable baseline for controllers using this control variable. Soot and NOx emissions are linked through a tradeoff. Generally, reducing one kind of emission leads to increased production of the other pollutant. As a result both emission types have to be considered for the optimization process. To take this into account, a weighted cost function of the integral emissions is used to determine optimal solutions depending on the weighting factor c. The choice of c determines whether the minimization focus is on reducing soot or NOx emissions. The cost function is defined by J(u) =

 TE 0

c · qSoot (u(t),t) + qNOx (u(t),t)dt

(1)

where J is the value of the cost function, u the control input sequence, t the time in s, TE is the finishing time of the experiment, qSoot is the measured soot rate in kg/s and qNOx is the NOx rate in kg/s. The influence of the control input on the soot and NOx emissions is modeled using measurement data. 3.2 Choice of control variable As stated in the introduction, the rail pressure pRail was chosen as a control input. The influence of different control variables such as the VGT opening XVGT , the EGR valve position XEGR , throttle valve opening XTHR , swirl valve opening XSWR , injection angle φMI , pilot injection timing tiPI or pilot fuel mass qPI have been analyzed.

Fig. 1. Schematic of the LII sensor and sensor head from Zhang (2017) Used sensors for soot measurement • LII: Prototype for laser based in-situ measurements, time delay < 0.2s, highly dynamic response, limited accuracy, noisy, no filtering or averaging • AVL Opacimeter: Standard device for dynamic measurements, dynamic response, slightly filtered signals, time delay about 0.4s 160

In order to choose the suitable control variable, an experimental sensitivity analysis was performed. To this end, static offsets ∆x of ±20% of its range were applied to the value used by the ECU. The integral emission deviations ∆Soot and ∆NOx were measured and compared together with the impact on the produced torque ∆T. Due to the strong impact, the air path variables VGT and EGR were only shifted by ±10% to avoid saturation of the exhaust sensors and to guarantee safe operating conditions. Moreover the dynamic behaviour of the control variables was examined since a fast response is desirable for the following applications. Although VGT and EGR have a strong impact on the emissions they were not used due to their significant delay times. The rail pressure was chosen due to its combination of a fast response time and a relevant impact on the emission tradeoff.

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Table 1. Sensitivity of the integral total emissions in comparison to the ECU reference Control variable XEGR XEGR XVGT XVGT XTHR XTHR XSWR XSWR φMI φMI tiPI tiPI qPI qPI pRail pRail

∆x +10% −10% +10% −10% +20% −20% +20% −20% +1◦ −1◦ +200µs −200µs +0.5mg −0.5mg +200bar −200bar

∆Soot +325% −57% +250% −26% 5% −1% −21% +6% −3% +2% −17% −2% +18% −32% −24% +35%

∆NOx −57% −78% −45% +47% −1% 2% +8% −1% +9% −7% +2% −2% 0% 3% +21% −19%

∆T −8% +3% −9% +2% 0% 0% 0% 0% 0% −1% −3% −1% +2% −4% +3% −2%

Fig. 2. Segment of a soot model Soot Opacity model validation

0

To simplify the model complexity resulting from varying sensitivities due to changes in the engine’s internal conditions over the test cycle, such as temperatures, pressure or speed, time-varying models are used, in which the time is the only parameter to describe the current internal conditions. This is only suitable for repeatedly running the exact same test scenario as the models are only suitable for specific conditions at a specific time. Shifting the ECU reference signal by constant offsets allows to create static time-varying models describing the influence of rail pressure variations in comparison to the ECU trajectory on the emissions over time ∆qSoot (∆pRail (t),t) and ∆qNOx (∆pRail (t),t) where ∆qSoot = qSoot − qSoot,ref is the soot difference to a reference measurement with the standard ECU, ∆qNOx = qNOx − qNOx,ref is the NOx difference to a reference measurement with the standard ECU and ∆pRail = pRail − pRail,ref is the rail pressure variation in comparison to the ECU trajectory. A segment of a static time-varying soot model is depicted in Fig. 2. For modeling the influence on the soot emissions, both LII measurement data or Opacimeter signals can be used, resulting in slightly different models. All measurements are calibrated to mg/s using the Micro Soot Sensor and exhaust gas measurements such as mass and volume flow to get emission rates. Those rates can be integrated over time in order to get total emissions which are relevant due to legal limitations. The models were validated by comparing actual measurement results to model predictions. An exemplaric example is depicted in Fig. 3 to Fig. 5.

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In order to use the above defined cost function for calculating optimal input trajectories, models describing the impact of the control variable, the rail pressure, on the outputs soot and NOx rates are needed. Based on measurement results, the influence is modeled statically in this work since the rail pressure affects the creation of emissions almost instantaneously without noticeable delays.

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Fig. 5. Exemplaric input trajectory used for model validation

Fig. 6. Optimal rail pressure for segment of the WLTP

3.4 Optimization The cost function can be minimized step-wise for every sample since all models used are only static and no dynamic constraints are used. Calculating the optimal solution is therefore simple. The overall minimization trajectory is the concatenation of the single minimization solutions for every sample. x(∆pRail (t),t) = c · ∆qSoot (∆pRail (t),t) + ∆qNOx (∆pRail (t),t) J(∆pRail ) =

 TE 0

(2)

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∆pRail,Opt = arg min J(∆pRail ) ∆pRail

min J(∆pRail ) =

∆pRail

 TE 0

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min x(∆pRail (t),t)dt (5)

∆pRail (t)

⇑ static model → stepwise minimization ∆pRail,Opt (t) = min x(∆pRail (t),t) ∆pRail (t)

(6)

Applying the optimal trajectories resulting from the optimization in 3.4, such as the one depicted in Fig. 6, to the engine allows to reduce the total emissions. The relative reduction potential ∆qSoot ∆qNOx ∆qˆSoot = and ∆qˆNOx = qSoot,ref qNOx,ref compared to the standard ECU performance is shown in Table 2 for different weighting factors. The results are averaged values from multiple test runs to limit the influence of stochastic deviations in the engine’s behaviour and external conditions such as air pressure and humidity. The results in Table 2 show that significant soot reductions are possible using offline optimization. The NOx emissions, however, increase due to the existing trade off. Table 2. Relative deviations of the integral total emissions in comparison to the ECU reference ∆qˆSoot in % −55 −62 −68

4.1 Controller Since the optimal control is not real time capable, a feedback controller is used to determine that soot reduction is possible even without information about the future test cycle. For this work, a simple control strategy was used since the basic controller already recovers most of the reduction potential of the offline optimization. A proportional controller in combination with gain scheduling to enable different gains for different operating points was used, which sets the input ∆pRail proportional to the measured soot signal. ∆pRail (t) = P(qSoot (t)) · qSoot (t)

3.5 Exemplaric results of the optimization

Weight factor c 6 12 24

4. FEEDBACK CONTROL

∆qˆNOx in % 3.6 6.2 10.2

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The gain P depends on the current soot level using a lookup table to get a suitable controller. This concept is based on the idea that increased soot rates can be reduced by raising the rail pressure in order to enhance fuel pulverization, mixture formation and therefore the internal combustion conditions. The drawbacks of this faster, cleaner and hotter combustion is an increased NOx production due to the temperature rise. The gains of the controller are tuned empirically in order to shape the input profile and the measured soot signals in accordance with the results from the offline optimization. Further tuning could possibly allow even better results. The feedback controller either uses the LII signals or measurement data from the Opacimeter to generate the control input. The performance using the different sensors is compared to evaluate possible advantages of the faster LII sensor in 4.2. Since the LII sensor is still a prototype, its measurement signal’s properties differ from those of the standard sensors. Especially the lack of signal processing and filtering leads to highly fluctuating signals. It is difficult to distinguish whether the fluctuations are due to measurement noise or real measurement information provided by the high sampling rate as no other comparably fast device is available. The ’noisy’ measurement signals result in large variations of the control input as depicted in Fig. 7, where a segment of the input profile from the feedback controller is compared to the standard input signal. The signals get much smoother when using the Opacimeter on the drawback of a limited dynamic due to the filtering and measurement principle.

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Fig. 11. NOx concentration for feedback controller using LII signals

The comparison in Fig. 12 with the optimal results from the offline optimization (green x) for the feedback controller using the LII (red +) and using the Opacimeter (blue ) demonstrate, that the possible emission reduction potential is exploited to a large extent. For comparison purposes the trade off of the standard ECU is depicted as well (black o). To determine these points the ECU reference is simply shifted by constant offsets to show the influence of different operating levels on the emission distribution.

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Fig. 9. Soot concentration measured by Mirco Soot Sensor for feedback controller using LII signals 4.2 Results using feedback controller with LII The results in Fig. 8 to Fig. 11 show, that the soot emissions, measured by the LII, Opacimeter and the Micro Soot Sensor are significantly lower in comparison to the reference, while the NOx is slightly higher. Additionally, the results demonstrate the higher variance of the LII signal but also its faster dynamics including more information about transient changes whereas especially the Micro Soot Sensor provides strongly filtered signals on costs of slow response characteristics.

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Especially when using the faster LII sensor the results are very close to the offline solution with additional soot reductions of about 5% in comparison to the use of the Opacimeter. This additional soot reduction potential is presumably due to the faster dynamics of the LII which allow better timed interventions. Sensor delay times are crucial for this control strategy, as only already produced emissions can be detected and on-time interventions are needed for significant emission reductions. Therefor minimal delay times are desirable. Overall soot reductions by 50% up to 60% can be realized using this method on cost of increasing NOx emissions by about 7%. The torque demand is fixed by the velocity profile of the cycle in combination with the used car model as the model calculates the torque demand only based on the current velocity and acceleration targets which are fixed by the test cycle. As a result the produced torque is only marginally affected by this control method since the gas pedal position is adjusted in order to track

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Fig. 12. Tradeoff results for different controllers compared to the ECU the demand. In a real car the minor torque variations would not be noticeable at all due to the high inertial mass. The fuel consumption, however, as a result of the gas pedal adaption, is reduced by about 0.7% in comparison to the standard reference due to a slightly increased efficiency through the increased rail pressure. 6. CONCLUSIONS In this work the optimization of the rail pressure profile is performed to reduce soot emissions of a diesel engine. In contrast to production type smoke limiters this approach improves combustion conditions by enhancing the mixture formation rather than limiting the injected fuel mass. This allows a more dynamic torque response while still reducing soot emissions. Different sensors are used to measure the soot concentration including the newly developed LII sensor, which provides beneficial dynamic behavior. To prevent a significant rise of NOx emissions, the soot-NOx-trade off is considered in the cost function for the optimization. The influence of the considered control variable, the rail pressure, on the emission rates are pictured using measurement results to build static time variant models. The results using optimal input trajectories from the offline optimization show that significant soot reductions by over 50% for the Worldwide Harmonized Test Cycle are possible, not only in simulation but also in real test. As a drawback the NOx emissions raise. Those soot reductions, however, cannot only be realized applying optimal input trajectories but also when using a closed loop controller. The developed controller uses feedback information by either the AVL Opacimeter or the LII sensor to set the rail pressure and is able to exploit the soot reduction potential to a large extent. Using the new, faster LII sensor enables an even better performance in comparison to the use of the Opacimeter with averagely additional 5% soot reduction. Concluding, feedback control of the rail pressure is a suitable method to reduce soot emissions and the LII sensor with its beneficial dynamic properties is a promising candidate for future use. REFERENCES Alberer, D. and del Re, L. (2009). Fast oxygen based transient diesel engine operation. SAE International Journal of En164

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