Control Applied to a Reciprocating Internal Combustion Engine Test Bench Under Transient Operation: Impact on Engine Performance and Pollutant Emissions

energies

Article Control Applied to a Reciprocating Internal Combustion Engine Test Bench under Transient Operation: Impact on Engine Performance and Pollutant Emissions

Ismael Payo ID , Luis Sánchez ID , Enrique Caño ID and Octavio Armas * ID

Escuela de Ingeniería Industrial, Campus de Excelencia Internacional en Energía y Medioambiente, Universidad de Castilla La Mancha, Ediﬁcio Sabatini, Av. Carlos III, s/n, 45071 Toledo, Spain; [email protected] (I.P.); [email protected] (L.S.); [email protected] (E.C.) * Correspondence: [email protected]

Received: 8 September 2017; Accepted: 17 October 2017; Published: 25 October 2017

Abstract: This work presents a methodology to adjust the electronic control system of a reciprocating internal combustion engine test bench and the effect of the control parameters on emissions produced by the engine under two extreme situations: unadjusted and adjusted, both under transient operation. The aim is to provide a tuning guide to those in charge of this equipment not needed to be experts in control engineering. The proposed methodology covers from experimental plant modelling to control parameters determination and experimental validation. The methodology proposed includes the following steps: (i) Understanding of test bench and mathematical modeling; (ii) Model parameters identification; (iii) Control law proposal and tuning from simulation and (iv) Experimental validation. The work has been completed by presenting a comparative experimental study about the effect of the test bench control parameters on engine performance profiles (engine speed, engine torque and relative fuel air ratio) and on regulated gaseous emissions (nitrogen oxides and hydrocarbons concentrations) and the profile of number of particles emitted. The whole process, including experimental validation, has been carried out in a test bench composed of a turbocharged, with common rail injection system, light duty diesel engine coupled to a Schenck E-90 eddy current dynamometric brake and its related Schenk X-act control electronics. The work demonstrates the great effect of the test bench control tuning under transient operation on performance and emissions produced by the engine independently of the engine accelerator position demanded before and after the test bench tuning.

Keywords: PID control; test stand; diesel engine; dynamics; methodology; dynamometer braking system

1. Introduction Nowadays, the applications of electronic control to the operating processes of engines and vehicles are varied. Fathi et al. [1] have recently published an important review about works related to modelling and design of controller structure for controlling the known homogenous charge compression ignition. This work outlines, among others, from works dedicated to the denominated black-box modelling based on artiﬁcial networks [2] or based on system identiﬁcation models [3] to those works about current controllers based on both linear [4,5] and non-linear models [6,7]. Other works have focused the application of electronic control techniques directly on Diesel engines with the objective of reducing fuel consumption and pollutant emissions by means of a switching strategy between both the high and low pressure exhaust gas recirculation systems [8] or

Energies 2017, 10, 1690; doi:10.3390/en10111690 www.mdpi.com/journal/energies Energies 2017, 10, 1690 2 of 17 acting on the exhaust gas recirculation (EGR) control strategy [9] or simply improving the intake air control system of engines [10,11]. In other cases, different EGR and injection strategies have been compared in order to study the effect of biodiesel fuels on performance and emissions of Diesel light-duty engines [12]. No less important have been those works related to the assessment of other types of techniques, as the use of biharmonic maps, to predict performance and emissions of vehicles working in captive fleets. In this work authors indicated the possibility of introducing the use of this technique in ECU operation [13]. Currently, although the literature dedicated to electronic control applied to the internal combustion engines is extensive, literature focused on the control system of the test bench itself (the engine + dynamometer brake set) is really insufficient. Included in this group of works, for example, Gotfryd [14] worked on the adaptation of an existing direct current (DC) dynamometer to transient operation in order to facilitate at least preliminary transient tests carried on for low-budget engine development purposes, while other authors worked directly on the improvement of the test bench control system [15–17]. Recent works have been published on the control of engine test benches, related to different studies cases: in [18] the authors proposed a nonlinear output feedback controller based on a reduced order observer to solve a set point tracking problem of a combustion engine test bench; in [19] an active vibration damping method through specific control of the dynamometric brake based on a Kalman filter was proposed; and in [20] a test bench control was simulated, including, in addition to the combustion engine test bench, the transmission model and the dynamic of the vehicle. The present work can be included in the last mentioned group of works. In this case a combined theoretical-experimental methodology for the optimization of the control system of a reciprocating engine test bench is presented. The work done demonstrates the impact of the regulation of the engine test bench control system on performance and pollutant emissions produced by a light duty common rail diesel engine during different sequences under transient operation. Methodology proposed is integrated by different parts which includes from the plant (engine-dynamometer) modelling until the effect of the control system before and after the optimization on performance and main regulated pollutant emissions. Results show the great effect of the engine test bench control system on emissions if it is regulated without optimization. Finally, as collateral result, the methodology has additional advantage: its easy implementation by researchers without wide experience in optimization of automatic control systems.

2. Experimental Installation Figure1 shows a complete scheme of the experimental installation used in this work. The work was carried out in a test bench mainly composed by an engine coupled to a dynamometric brake. The engine was a four-stroke, 2.2 L, light-duty Diesel engine (NISSAN, Nissan Technical Centre Europe (NTCE), Barcelona, Spain) with four cylinders, turbocharged, intercooled and with a common-rail injection system. This engine (see main speciﬁcations in Table1) is representative of those typical used in Diesel vehicles in Europe. The engine was instrumented to measure several key temperatures and pressures (intake air, fuel, exhaust gases, lube oil, etc.). A piezoelectric pressure transducer (6056A, Kistler, Winterthur, Switzerland) coupled to a charge ampliﬁer (Kistler 5018A, Winterthur, Switzerland) was used for recording instantaneous in-cylinder pressure. Crankshaft rotational speed and instantaneous piston position were determined with two possibilities: (i) by means of an angle encoder (Kistler 2614CK, Winterthur, Switzerland) with a resolution of 720 pulses per revolution and (ii) by means of the INCA PC (measurement, calibration and diagnostic software published by ETAS, Stuttgart, Germany) directly registered from the engine Electronic Control Unit (ECU). The data were recorded with a digital scope (DL708E, Yokowaga, Singapore) and transferred to the computer via general purpose interface bus (GPIB). Energies 2017, 10, 1690 3 of 17 Energies 2017, 10, 1690 3 of 17

Figure 1. General scheme of the experimental installation. Figure 1. General scheme of the experimental installation.

Table 1. Main engine characteristics. Table 1. Main engine characteristics. Type Diesel, Direct Injection TypeModel Nissan Diesel, YD22 Direct Injection Total displacement 2.2 L CylinderModel arrangement 4 cylinders, Nissan in line YD22 Total displacement 2.2 L Bore/stroke 86.5/94 mm Cylinder arrangement 4 cylinders, in line Compression ratio 16.7:1 Bore/stroke 86.5/94 mm Intake system Turbocharged with intercooler Compression ratio 16.7:1 IntakeEGR system system TurbochargedHot, high pressure with intercooler FuelEGR injection system system Common rail, Hot, pilot high injection pressure FuelMaximum injection rated system power 82 Common kW at 4000 rail, min pilot−1 injection MaximumMaximum rated rated power torque 248 Nm82 at kW 2000 at 4000min−1 min −1 Maximum rated torque 248 Nm at 2000 min−1 The engine was coupled to a Schenck E-90 eddy current dynamometer (Horiba Schenck, Landwehrstrasse 55, D-64293, Darmstadt, Germany). The brake control system allowed to measure The engine was coupled to a Schenck E-90 eddy current dynamometer (Horiba Schenck, and/or control the engine speed (ω), accelerator position (α) and engine torque (M). Maximum speed Landwehrstrasseof the dynamometer 55, D-64293, is 12,000 Darmstadt, min−1 while Germany).the maximum The torque brake is control200 Nm systemat 90 kW allowed [21,22]. The to measure test and/orbench control has an the X-Act engine brake speed control (ω system), accelerator [23,24]. A position widely (usedα) and controller engine that torque implements (M). Maximum the control speed −1 of thescheme dynamometer described is in 12,000 this work min (seewhile Appendix the maximum B). This controller torque is is200 connected Nm at 90 with kW a[21 accelerator,22]. The test benchposition has an controller. X-Act brake control system [23,24]. A widely used controller that implements the control scheme describedA digital encoder in this coupled work (seeto the Appendix engine crankshaftB). This shaft controller allowed is to connected measure engine with speed a accelerator with positionresolu controller.tion 720 pulses per turn. Electronic transducer gives measured engine speed signal (ω). A digitalA load encoder cell mounted coupled on tothe the engine engine-brake crankshaft coupling shaft shaft allowed allowed to to measureregister the engine steady speed state with resolutionengine 720torque pulses signal per (M turn.) with Electronic resolution transducer0.4 Nm. Measurement gives measured error due engine to temperature speed signal is less (ω ).than 2A Nm load if temperature cell mounted varies on the less engine-brake than 10 °C from coupling calibration. shaft allowed to register the steady state engine In addition, in order to see the effect of control parameters of the test bench on pollutant torque signal (M) with resolution 0.4 Nm. Measurement error due to temperature is less than 2 Nm if emissions produced by the engine under a typical transient sequence, both, instantaneous temperature varies less than 10 ◦C from calibration. In addition, in order to see the effect of control parameters of the test bench on pollutant emissions produced by the engine under a typical transient sequence, both, instantaneous concentration Energies 2017, 10, 1690 4 of 17

of gaseous pollutant emissions (nitrogen oxides, NOx and unburned total hydrocarbons, THC) and instantaneous particle concentration were registered. Unburned total hydrocarbon emissions were measured with a ﬂame ionization detector (model GRAPHITE 52M (Environnement, 111, Bd Robespierre—BP 84513, Poissy, France)) and a chemiluminiscence analyzer (model TOPAZE 32M (Environnement, 111, Bd Robespierre—BP 84513, Poissy, France)) was used to measure NOx emissions. Instantaneous particle concentration was measured by means of a PEGASOR Particle Sensor (VP Sales & Marketing Pegasor Ltd., Tampere, Finland) model PPS-M. As test fuel was used an ultra-low sulfur EN-590 Diesel fuel. Main properties of the test fuel are shown in Table2.

Table 2. Properties of the test fuel.

Properties Diesel

Molecular formula C15.18H29.13 Molecular weight (g/mol) 211.4 H/C Ratio 1.92 Stoichiometric fuel/air ratio 1/14.64 C (% w/w) 86.13 H (% w/w) 13.87 O (% w/w) 0 Density at 15 ◦C (kg/m3) 843 Viscosity at 40 ◦C (cSt) 2.97 Lower Heating Value (MJ/kg) 42.43 Cetane number 54.2 Cold ﬁlter plugging point (CFPP) (◦C) −17 Distillation (vol.) 10% 207.6 50% 278.2 90% 345.0

3. Methodology The main goals of the test bench control system are to adjust brake opposition torque and speed, both under both steady and transient operating modes. Under transient operation, control avoids overshooting and warranties settle time of controlled signals. Under steady-state operating mode, control minimizes error between required and measured controlled signals. These goals are obtained by two uncoupled control laws [16] of engine torque and engine speed. Control laws are obtained through a process of modeling and simulation of the whole test bench. A reciprocating internal combustion engine is a complex thermodynamic system. Its performance depends on many different factors as density and temperature of the intake air, pressures and temperatures of several components of the engine, quality of the fuel, etc. Nevertheless, in a simple way the engine can be modeled as a linear first order system, although non linearity introduced by some elements such as the turbocharger and the electronic control unit (ECU) [17] have to be considered when analyzing simulation results. Main signals to define the test bench operating mode are the following: (i) accelerator position (α), measured in %; (ii) Engine speed (ω), measured in min−1 and (iii) Load cell torque, measured α in Nm. The test bench control mode selected in this work has been ω mode. In this mode, the input parameter is α and the demand is ω. Control acts on the brake to adjust the effective torque MF (opposition torque generated by the brake) to obtain the required ω. In this section, a procedure for tuning the test bench control system is proposed. The main steps of this procedure can be summarized as follows: (i) Identification of the mechanical plant structure (test bench components and working modes); (ii) Modelling the mechanical plant and identification of its parameters and (iii) Obtaining the values of the plant control parameters. In this work we consider that control laws are implemented in the control system and we are allowed just to adjust them. Energies 2017, 10, 1690 5 of 17

Energies 2017, 10, 1690 5 of 17 From the point of view of control, Figure2 shows, as a summary, the structure of the main parts of the plantFrom (test the bench) point of presented view of control, in Figure Figure1. Main 2 shows components, as a summary, are the the engine structure and of the the dynamometricmain parts of the plant (test bench) presented in Figure 1. Main components are the engine and the brake. Behavior of the system is controlled by the control system. Figure2 also shows the interactions dynamometric brake. Behavior of the system is controlled by the control system. Figure 2 also shows or communicationsthe interactions or between communications all parts between of the plant. all parts of the plant.

FigureFigure 2. Scheme2. Scheme of of the the plantplant structure and and communications communications..

4. Plant Parameters 4. Plant Parameters This part shows a summary of the experimental procedure used to obtain the values of the plant Thisparameters. part shows Appendix a summary A shows of the the theor experimentaletical development procedure of the used plant to obtainmathematical the values model. of theThis plant parameters.model is Appendix presented Ain showsAppendix the A theoretical (Equation ( developmentA7)) and defines of the the dynamical plant mathematical behavior of a model. system This modelcomposed is presented by a Diesel in Appendix engine coupledA (Equation to a dyna (A7))mometric and defines brake. the Values dynamical of parameters behavior A and of B a are system composeddifferent by for a Dieseleach test engine bench coupledand need to be a dynamometricidentified through brake. experimentation. Values of parameters Proposed procedure A and B are differentfor parameters for each test identification bench and can need be described to be identified as follows. through experimentation. Proposed procedure α In the test bench control mode ⁄ (control system adjusts the effective torque (MF) and α is for parameters identification can be describedMF as follows. Ingiven the as test engine bench input) control, set modedemandedα (control effective system torque adjuststo zero. If the MF effective= 0, Equation torque (A(7M) yields:) and α is given MF F as engine input), set demanded effective torque to zero.Â If MF = 0, Equation (A7) yields: ω(s) = (1) s + B Aˆ where Â: ω(s) = (1) s + B Â = A(α(s) − αC(s)) (2) ˆ whereand:A: Aˆ = A(α(s) − αC(s)) (2) MC(s) α (s) = (3) and: C K MC(s) where α is the accelerator position neededα to(s compensate) = the Coulomb drag. (3) C C K Equations (1) and (2) show that the engine speed (ω) is related to accelerator position (α) and whereCoulombαC is the drag accelerator can be considered position as needed a perturbation to compensate input to the system. Coulomb drag.

EquationsParameters (1) and A, Â (2), αC show and B thatare obtained the engine studying speed the(ω time) is relatedresponse to of accelerator the system positionto a set of(α α) and Coulombstep inputs dragcan [25]. be A consideredset of Â and asB parameters a perturbation are obtained input to for the each system. experiment and a final A and B Parametersparameters areA ,obtainedAˆ , αC and for theB are system obtained from E studyingquations (1) the and time (2). response Figure 3 shows of the the system actualto figures a set of α step inputsfor a set [ 25of ].three A set experiments of Aˆ and. B parameters are obtained for each experiment and a ﬁnal A and B parameters are obtained for the system from Equations (1) and (2). Figure3 shows the actual ﬁgures for a set of three experiments. The A parameter is the slope of the set of Aˆ parameter obtained for each accelerator position. αC is the zero crossing of the set of experiments while final B value is obtained as the mean value of the set of B parameters obtained for each experiment. Finally, modelled and experimental results are compared. Energies 2017, 10, 1690 6 of 17 Energies 2017, 10, 1690 6 of 17

Figure 3. Relationship between accelerator position (α)) and Âˆ parameter. parameter.

5. ControllerThe A parameter Parameters is the slope of the set of Â parameter obtained for each accelerator position. αC is the zero crossing of the set of experiments while final B value is obtained as the mean value of the This part describes how to obtain the values of the test bench control scheme shown in AppendixB. set of B parameters obtained for each experiment. Finally, modelled and experimental results are Design requirements are also presented in AppendixB. compared. 5.1. Prefeed 5. Controller Parameters Prefeed gain Kω is obtained as the inverse of the plant static gain when effective torque is consideredThis part the describes control variable. how to obtain Prefeed the term values equals of the measured test benchω control(t) and scheme demanded shownω∗ in(t) Appendixoutput in B.steady Design state requirements and only if identiﬁedare also presented plant model in Appendix ﬁts exactly B the. actual plant and no perturbations affects the system: 5.1. Prefeed K·B Kω = (4) A Prefeed gain Kω is obtained as the inverse of the plant static gain when effective torque is ∗ considered5.2. I-PD Controller the control variable. Prefeed term equals measured ω(t) and demanded ω (t) output in steady state and only if identified plant model fits exactly the actual plant and no perturbations affects the systemFrom: Appendix B Equation (A8), the transfer function of a classical PID (with the same expression if ω∗(t) = 0, as the I-PD) is: K · B Kω = (4) K A K + K s + K s2 R(s) = I + K + K s = I P D (5) s P D s 5.2. I-PD controller If the same real value is given to both zeros: From Appendix B Equation (A8), the transfer function of a classical PID (with the same 2 0 0 0 expression if ω∗(t) = 0, as the I-PD) is: (s + a) k s2 + 2k as + k a2 R(s) = k0 = (6) s s 2 KI KI + KPs + KDs R(s) = + K + K s = (5) and the expressions for each parameters are: P D s

If the same real value is given to both zeros: 0 KP = 2ak (7) (s + a)2 k′s2 + 2k′as + k′a2 R(s) = k′ = 2 0 (6) s KI = a k s (8) and the expressions for each parameter are: 0 KD = k (9) From Equations (7)–(9), regulator parametersKP = 2ak′ depends on gain k0 and double zero a. If only(7) 2 effective torque is considered as plant input, theKI = controlleda k′ system is shown in Figure4, and system(8) characteristic equation yields: KD = k′ (9)

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EnergiesFrom2017 ,E10quations, 1690 (7)–(9), regulator parameters depends on gain k′ and double zero a. If 7 only of 17 effective torque is considered as plant input, the controlled system is shown in Figure 4.

s2 + 2as + a2A 1 + k0 = 0 (10) Ks(s + B) K + k0As2 + KB + k0A2as + k0Aa2 = 0 (11) Energies 2017, 10, 1690 7 of 17 Because no overshooting and the fastest response time are design speciﬁcations, closed loop

systemFrom needs E aquations double real (7)– pole.(9), regulator Figure5 shows parameters a qualitative depends system on rootgain locusk′ and depending double zero of the a. valueIf only ofeffective “a” where torque k’ is is the considered variable gain. as plantFigure input, 4. PID th controllede controlled system system. is shown in Figure 4. and system characteristic equation yields: (s2 + 2as + a2)A 1 + k′ = 0 (10) Ks(s + B) (K + k′A)s2 + (KB + k′A2a)s + k′Aa2 = 0 (11) Because no overshooting and the fastest response time are design specifications, closed loop system needs a double real pole. FigureFigure 5 shows 4. PID controlleda qualitative system system. root locus depending of the value of “a” where k’ is the variable Figuregain. 4. PID controlled system. and system characteristic equation yields: (s2 + 2as + a2)A 1 + k′ = 0 (10) Ks(s + B) (K + k′A)s2 + (KB + k′A2a)s + k′Aa2 = 0 (11) Because no overshooting and the fastest response time are design specifications, closed loop system needs a double real pole. Figure 5 shows a qualitative system root locus depending of the value of “a” where k’ is the variable gain.

(a)

(b)

FigureFigure 5. 5. ((aa)) Root Root locus locus when when a a > > B B and and ( (bb)) Root Root locus locus when when a a < < B. B.

(b)

Figure 5. (a) Root locus when a > B and (b) Root locus when a < B.

Energies 2017, 10, 1690 8 of 17

If a < B, the only location of the real axis where both poles of the closed loop system can be placed is “a” and this implies an inﬁnite gain k’. For this reason this case will not be considered for further development. Figure5b shows the case when a > B. In this case, output from the real axis of root locus branches is the only one valid location for a double pole of the closed loop system. In this case as bigger becomes the “a” value, bigger becomes the control gain and, as a result, bigger becomes the control variable (effective torque) which is not a desirable situation. This last case (a > B) and not enough big “a” will be evaluated in following paragraphs. Gain k0 can be obtained from Equation (11) as:

q 2 −KB + k0A2a ± KB + k0A2a − 4K + k0Ak0Aa2 s = (12) 2K + k0A when: 2 KB + k0A2a − 4K + k0Ak0Aa2 = 0 (13) or: K2B2 + 4KAa(B − a)k0 = 0 (14) and ﬁnally: KB2 k0 = − (15) 4Aa(B − a) that, when a > B becomes: KB2 k0 = (16) 4Aa(a − B)

5.3. Decoupling Gain The decoupling gain K is used to decouple (cancel) the effect of the α in control variable (effective torque). As can be seen in AppendixA: K = JA (17) where J is the inertia of the set composed by the engine + dynamometer, from manufacturer data, and A is obtained as described in previous paragraphs.

6. Results and Discussion

6.1. Results from the Plant Simulation and Its Experimental Validation Previously described methodology has been applied to a theoretical system considering a normalized plant. This means parameters A, B and K equals 1 and every variable in the model ranges from 0 to 1. Engine speed demand (ω∗) and accelerator position (α) have been taken as step signals with arbitrary final value (ω∗ = 0.2 and α = 0.5). Also an arbitrary value has been chosen for Coulomb drag (MC = 0.05). To avoid step changes in effective torque, a first order filter has been used to smooth step shape of input signals ω∗ and α. Control law parameters (Kω, KP, KI, KD) have been obtained from Equations (4), (7)–(9) and (17) when a > B. As an example, setting a = 1.5B gives values such as KP = 1, KI = 0.749, KD = 0.333. Figure6 shows simulated engine speed and effective torque obtained with previous values. Experimental validation of the presented methodology has been carried out in facilities presented in Figure1. This installation is operative in the laboratory managed by the Energy and Environmental Processes Investigation Group (www.grupogpem.com). The procedure followed to obtain the experimental results shown in next paragraphs has been as described in previous sections. Plant parameters A and B (Equations (2) and (3)) have been identified from several sets of experimental data. Energies 2017, 10, 1690 9 of 17

Energies 2017, 10, 1690 9 of 17 Energies 2017, 10, 1690 9 of 17

(a)

(b)

Figure 6. Results obtained from the plant simulation (a) engine speed; (b) effective torque.

Experimental validation of the presented methodology has been carried out in facilities presented in Figure 1. This installation is operative in the laboratory managed by the Energy and Environmental Processes Investigation Group (www.grupogpem.com(b) ). The procedure followed to obtain the experimental results shown in next paragraphs has been Figure 6. Results obtained from the plant simulation (a) engine speed; (b) effective torque. as describedFigure in 6. previousResults obtained sections. from Plant the plantparameters simulation A (anda) engine B (Equations speed; (b) effective (2) and torque. (3)) have been identifiedExperimental from several validation sets of experimental of the presented data. methodology has been carried out in facilities ̂ˆ presentedFirst, AAin and andFigureB Bwere were 1. This obtained obtained installation using using time is timeoperative domain domain experiments. in the experiments laboratory System. managed System response response by to athe pulse Energy to sequence a pulse and seqEnvironmental(seeuence Figure (see7) was Figure Processes evaluated. 7) was Investigation Valuesevaluated. of 21.5,Group Values 22 ( andwww.grupogpem.com of 21.5, 22.5 %22 of and accelerator 22.5 %). of position accelerator were position tested. These were tested.testsThe were These procedure performed tests were followed without performed to load obtain without (effective the experimentalload torque (effective = 0 Nm), results torque i.e., shown the= 0 Nm), opposition in nexti.e., the paragraphs torque opposition generated has torque been by generatedasthe described dynamometric by inthe previousdynamometric brake was sections. zero. brake ThePlant was minimum parameters zero. The accelerator minimum A and B position (Equationsaccelerator to compensate position (2) and (3))to the compensate have Coulomb been theidentifiedfriction Coulomb is 19.06%from friction several (as Sectionis sets19.06% of 5.1 experimental (asshows). Section On 5.1 data. the shows). other hand, On the the other maximum hand, acceleratorthe maximum position accelerator tested positionwithoutFirst, loadtested Â and (null without B effective were load obtained torque)(null effective was using 23% torque) time because domain was from 23% experiments this because value from the. System enginethis value response speed the increasesengine to a speed pulse a lot increasesseqanduence exceeds a( seelot the andFigure torque exceeds 7) limitwas the ofevaluated. torque the test limit bench. Values of the Elapsed of test 21.5, bench. time22 and Elapsed for these22.5 %time values of foraccelerator wasthese ﬁtted values position in 60was s whilefittedwere intested.time 60 betweens whileThese ttestsime values betweenwere was performed ﬁtted values in 30was without s andfitted the load in engine3 0(effective s and was the workingtorque engine = was0 in Nm), idle working condition.i.e., the in opposition idle condition torque. generated by the dynamometric brake was zero. The minimum accelerator position to compensate the Coulomb friction is 19.06% (as Section 5.1 shows). On the other hand, the maximum accelerator position tested without load (null effective torque) was 23% because from this value the engine speed increases a lot and exceeds the torque limit of the test bench. Elapsed time for these values was fitted in 60 s while time between values was fitted in 30 s and the engine was working in idle condition.

(a)

Figure 7. Cont.

(a)

Energies 2017, 10, 1690 10 of 17 Energies 2017, 10, 1690 10 of 17

Energies 2017, 10, 1690 10 of 17

(b)

FigureFigure 7. 7. ExperimentalExperimental signals signals of (a of) accelerator(a) accelerator position(b) position and (b) engine and (b speed) engine used speed under experimental used under experimental validation of the model. validationFigure 7. of Experimental the model. signals of (a) accelerator position and (b) engine speed used under experimental validation of the model. Numerical fitting procedure proposed by Levenberg-Marquardt [26,27] has been used to obtain Numerical ﬁtting procedure proposed by Levenberg-Marquardt [26,27] has been used to obtain Aˆ Â and B values. Table 3 shows the obtained values for each accelerator position. and B values.Numerical Table fitting3 shows procedure the obtained proposed values by Levenberg for each accelerator-Marquardt position.[26,27] has been used to obtain Â and B values. Table 3 shows the obtained values for each accelerator position. Table 3. Â and B values for each accelerator position. Table 3. Aˆ and B values for each accelerator position. Table 3. Â and B values for each accelerator position. Accelerator Position (%) Aˆ B Accelerator Position (%) Aˆ B Accelerator21.5 Position (%) 650.4Aˆ 0 0.568B 21.52221.5.0 650.40650.4810.010 0.5680.645 0.568 22.022.522.0 810.01810.01903.88 0.6450.592 0.645 22.522.5 903.88903.88 0.592 0.592 New values of Â and B are obtained for each accelerator position. “A” value is obtained as the ̂ slopeNew ofN theew values linevalue relatings of ofA ˆA and Â andB areare accelerator obtained for forposition each each accelerator acceleratorand αC is theposition. position. zero crossing “A “”A value” value of theis isobtained line, obtained as statedas asthe the in Â α previousslopeslope of of the secti the line lineon relating. Figurerelating A8ˆ andshows and acceleratoraccelerator the values position obtained and forαCC isAis theand the zero α zeroC in crossing crossingexperiments. of of the the line, B line, is as obtained asstated stated in as in previous section. Figure 8 shows the values obtained for A and α in experiments. B is obtained as theprevious mean section.value of Figure B from8 showsTable 3 the. values obtained for A and αCCin experiments. B is obtained as the meanthe mean value value of Bfrom of B from Table T3able. 3.

FigureFigure 8.8. ObtainedObtained Âˆ and and Coulomb Coulomb drag drag.. Figure 8. Obtained Â and Coulomb drag.

Finally, the values for the setup were: A = 250.45, B = 0.56 and α = 19.06%. Figure 9 shows Finally,Finally, the valuesvalues forfor thethe setup setup were: wereA: A==250.45250.45, B, B==0.560.56and andα CαC ==19.06% 19.06%. Figure. Figure9 shows 9 shows the the response of both the actual system and the identified model. C theresponse response of both of both the the actual actual system system and and the the identiﬁed identified model. model.

Energies 2017, 10, 1690 11 of 17 Energies 2017, 10, 1690 11 of 17

FigureFigure 9. 9Experimental. Experimental andand modelledmodelled signals after after plant plant parameters parameters identification identiﬁcation..

6.2. Results from the Engine Tested before and after the Test Bench Tuning 6.2. Results from the Engine Tested before and after the Test Bench Tuning Test bench control electronics is an X-act brake controller and it is able to implement the control Test bench control electronics is an X-act brake controller and it is able to implement the control laws proposed in this work. Control parameters (K, Kω, KP, KI, KD) were obtained from Equations laws proposed in this work. Control parameters (K, K ,K ,K ,K ) were obtained from Equations (4), (4), (7)–(9), (16) and (17). Parameters A and B were obtainedω P duringI D the plant identification process (7)–(9),and value (16) andJ was (17). obtained Parameters from the A andengine B were and obtainedbrake manufacturers. during the plantFinally, identification the definitive process data set and value J was obtained from the engine and brake2 manufacturers. Finally, the definitive data set for for the test bench tuning has been: J = 0.4 Kgm [24], K = 100.18, Kω = 0.22, KP = 0.22, KI = 0.09, 2 the test bench tuning has been: J = 0.4 Kgm [24], K = 100.18, Kω = 0.22, K = 0.22, K = 0.09, KD = 0.13. These values have to be intended as the first step of an experimentalP tuning, but Irather KDclose= 0.13 to final. These ones. values The previous have to values be intended of the controller as the first parameters step of an (manufacturing experimental calibration) tuning, but were: rather closeK = to20 final, Kω ones. = 0.22 The, KP previous= 0.22, K valuesI = 0.09 of, K theD = controller 0.01. parameters (manufacturing calibration) were: K = 20,For Kω demonstrating= 0.22, KP = the0.22, effect KI = of 0.09,the test KD bench= 0.01. tuning on performance and emissions, a transient testFor from demonstrating idle to an engine the effectspeed ofdemand the test of bench 1400 min tuning−1 and on accelerator performance position and emissions,from 0 to ~ a4 transient% was testcarried from idleout. to an engine speed demand of 1400 min−1 and accelerator position from 0 to ~4% was carriedThese out. actions produced the time evolutions of the engine speed before and after the test bench tuningThese presented actions producedin Figure 10 the. As time can be evolutions seen, before of thetuning, engine the speedtest bench before control and settings after the produced test bench tuninga great presented oscillation in of Figure the engine 10. As speed can be at seen,the end before of the tuning, transient the process test bench tested. control After settings the test produced bench a greattuning oscillation, the engine of speed the engine overshoot speed is less at the than end 10%. of the transient process tested. After the test bench tuning,The the evolution engine speed of the overshoot engine speed is less shown than in 10%. Figure 10 can be explained by the evolution of the loadThe cell evolution torque (control of the engineeffort) as speed consequence shown in of Figure the engine 10 can torque be explained and the inertia by the of evolution the rotating of the masses (axe and dyno) as Figure 11 shows. These torque oscillations could damage the coupling shaft load cell torque (control effort) as consequence of the engine torque and the inertia of the rotating between the engine and the dynamometer brake. masses (axe and dyno) as Figure 11 shows. These torque oscillations could damage the coupling shaft As Figure 11 shows, before the test bench tuning, a great peak of the load cell torque is produced between the engine and the dynamometer brake. by the peak of relative fuel-air ratio (great fuel-air mixture enrichment produced by the engine) trying As Figure 11 shows, before the test bench tuning, a great peak of the load cell torque is produced by to reach the desirable engine speed limit. Figure 12 shows the time evolution of the relative fuel-air the peak of relative fuel-air ratio (great fuel-air mixture enrichment produced by the engine) trying to ratio. reach the desirable engine speed limit. Figure 12 shows the time evolution of the relative fuel-air ratio. The great oscillations of relative fuel-air ratio produced negative consequences not only on both the engine speed and torque but also on pollutant emissions. Figure 13 shows the time evolution of nitrogen oxides (NOx), total hydrocarbons (THC) and particle concentrations before and after the test bench tuning.

Energies 2017, 10, 1690 12 of 17

Energies 2017, 10, 1690 12 of 17 Energies 2017, 10, 1690 12 of 17 Energies 2017, 10, 1690 12 of 17

Figure 10. Engine speed evolution before and after the test bench tuning. Figure 10. Engine speed evolution before and after the test bench tuning. Figure 10. Engine speed evolution before and after the test bench tuning. Figure 10. Engine speed evolution before and after the test bench tuning.

Figure 11. Load cell torque evolution (control effort) before and after the test bench tuning.

FigureFigure 11.11. Load cell torque evolutionevolution (control(control effort)effort) beforebefore andand afterafter thethe testtest benchbench tuning.tuning. Figure 11. Load cell torque evolution (control effort) before and after the test bench tuning.

Figure 12. Evolution of the relative fuel-air ratio before and after the test bench tuning.

Figure 12. Evolution of the relative fuel-air ratio before and after the test bench tuning. The greatFigureFigure oscillations 12.12. EvolutionEvolution of relative ofof thethe relativerelative fuel-air fuel-airfuel ratio-air ratioproducedratio beforebefore negative andand afterafter theconsequencthe testtestbench benchestuning. tuning not only. on both the engineThe great speed oscillations and torque of relativebut also fuelon pollutant-air ratio producedemissions. negative Figure 1 consequenc3 shows thees time not onlyevolution on both of The great oscillations of relative fuel-air ratio produced negative consequences not only on both nitrogenthe engine oxides speed (NO andx), torque total hydrocarbons but also on pollutant (THC) and emissions. particle concentrations Figure 13 shows before the timeand afterevolution the test of benchnitrogenthe engine tuning. oxides speed (NO andx), torque total hydrocarbons but also on pollutant (THC) and emissions. particle concentrations Figure 13 shows before the timeand afterevolution the test of benchnitrogen tuning. oxides (NOx), total hydrocarbons (THC) and particle concentrations before and after the test bench tuning.

Energies 2017, 10, 1690 13 of 17

(a)

(b)

(c)

Figure 13. Time evolution of (a) Nitrogen oxides (NOx); (b) Total Hydrocarbons (THC) and (c) particle Figure 13. Time evolution of (a) Nitrogen oxides (NO ); (b) Total Hydrocarbons (THC) and (c) particle concentrations before and after the test bench tuningx. concentrations before and after the test bench tuning.

As can be seen in Figure 13, values of NOx and THC concentrations show the effect of both test benchAs can tuning be seens even in when Figure the 13 time, values response of NO ofx theand gas THC analyzers concentrations is not enough show fast the as effect the tested of both testprocess bench tuningsrequires. even Particle when concentrations the time response is coherent of the with gas analyzers the values is of not the enough relative fast fuel as-air the ratios tested processregistered. requires. By the Particle other hand, concentrations after an increase is coherent of the ac withcelerator the valuesposition of from the relativeengine idle fuel-air operation ratios registered.to an operating By the mode other under hand, external after an load, increase initially, of the the accelerator effectiveness position of the fromDiesel engine Oxidation idle Catalyst operation

Energies 2017, 10, 1690 14 of 17 to an operating mode under external load, initially, the effectiveness of the Diesel Oxidation Catalyst (DOC) is lower because the initial temperature is lower than the light off temperature of the DOC. When the light off temperature increases then it begins the pollutant reduction. In this work, in both cases, the data recording ﬁnished before reaching the steady state thermal condition of the DOC. Despite the fact the engine received the same accelerator position order during the transient process tested, the test bench tuning imposes very different behavior on the engine, particularly on the relative fuel-air ratio and, in consequence, affecting pollutant emissions.

7. Conclusions This work has demonstrated the importance of using a suitable control law and adjusting the controller parameters in a reciprocating internal combustion engine test bench. Some engine performance proﬁles (engine speed, engine torque and relative fuel air ratio) and pollutant emissions (nitrogen oxides, hydrocarbons concentrations and number of particles emitted) have been compared under two different situations: unadjusted and adjusted control system. Great oscillations of relative fuel-air ratio disappear in the transient process by using an adjusted control system. This implies that the overshooting in the demanded engine speed and torque is reduced more than ﬁfty percent. Consequently, pollutant emissions are also reduced. A methodology has been proposed to adjust the control system, even for inexperienced professionals in control engineering in charge of this type of equipment. This methodology covers from experimental plant modelling to control parameters determination and experimental validation.

Acknowledgments: This study was carried out under the framework of the POWER Ref. ENE2014-57043-R project financed by the Spanish Ministry of Economy, Industry and Competitiveness. Authors also gratefully acknowledge the technical support provided by Nissan Europe Technology Centre, Spain. Author Contributions: Ismael Payo and Octavio Armas conceived and designed the experiments; Enrique Caño and Ismael Payo performed the experiments; Ismael Payo, Enrique Caño, Luis Sánchez and Octavio Armas analyzed the data and Ismael Payo, Enrique Caño, Luis Sánchez and Octavio Armas wrote the paper. Conflicts of Interest: The authors declare no conflicts of interest.

Appendix A. Mathematical Model of the Plant This Appendix shows the procedure followed to obtain the mathematical model of the set engine + dynamometer brake studied. Principle of conservation of angular torque describes the dynamics of the test bench:

. MM(t) + MF(t) − bω(t) − MC(t) = Jω (A1)

where: MM(t) is the engine torque (Nm), defined as torque generated by the engine. MF(t) is the effective torque (Nm), defined as opposition torque generated by the dynamometric brake. MC(t) is the Coulomb torque (Nm), defined as opposition torque due to the Coulomb drag. This is a term defined in [28]: MC(t) = KC × sign(ω(t)) (A2)

KC is the Coulomb drag constant. ω(t) is the engine speed (min−1), calculated as follows:

dθ(t) ω(t) = (A3) dt

θ(t) is the rotation angle of the engine crankshaft. J is the inertia of the set composed by the engine and the dynamometric brake. b is a viscous friction constant. Energies 2017, 10, 1690 15 of 17

In Laplace domain, Equation (A1) can be rewritten as: Energies 2017, 10, 1690 15 of 17

MM(s) + MF(s) − MC(s) = (Js + b)ω(s) (A4) MM(s) + MF(s) − MC(s) = (Js + b)ω(s) (A4) By considering the engine torque related to the accelerator position by means of: By considering the engine torque related to the accelerator position by means of:

MMM(sM)(=s)K=α(Ks)α (s) (A5(A5)) where K depends on the engine itself. where K depends on the engine itself. The modelled engine speed value can be obtained from (A4) and (A5): The modelled engine speed value can be obtained from (A4) and (A5):

Kα(s) + MF(s) − MC(s) = (Js + b)ω(s) (A6) Kα(s) + M (s) − M (s) = (Js + b)ω(s) (A6) and rearranging: F C and rearranging: MF(s) MC(s) A ω(s) = (α(s) + − ) (A7) KM (s) K M s(s+) B A ω(s) = α(s) + F − C (A7) K b K K s + B where: A = and B = J J = K = b where: AFigureJ 13and shows B theJ . blocks diagram of the plant model as in Equation (A7). Figure A1 shows the blocks diagram of the plant model as in Equation (A7). a

+ 1 + A MF w K s + B - 1 M K C

FigureFigureA1. A1.System System plantplant blocksblocks diagram.diagram.

AppendixAppendix B. B Control. Control System System Usually control electronics of an internal combustion engine test bench allows to work with Usually control electronics of an internal combustion engine test bench allows to work with different control modes. This Appendix describes the structure of the control system working in different control modes. This Appendix describes the structure of the control system working in accelerator position/engine speed (α⁄ ) mode. In this mode, the control variable is the brake effective accelerator position/engine speed ( α )ω mode. In this mode, the control variable is the brake effective torque and the output variable is ωthe engine speed. Main design requirements for the test bench torque and the output variable is the engine speed. Main design requirements for the test bench working in this mode are: working in this mode are:  Engine speed should reach the steady state as fast as possible but without overshooting nor in • Engine speed should reach the steady state as fast as possible but without overshooting nor in the the output variable neither in the controlled one. output variable neither in the controlled one.  Steady state error between demanded and measured regime should be zero even when transient • Steadyperturbations state error happens between. demanded and measured regime should be zero even when transient perturbations happens. Because the system plant can be described as a first order system (Equation (A7)), a PID controllerBecause is the able system to fulfill plant both can requirements: be described as a first order system (Equation (A7)), a PID controller is able to fulfill both requirements: t de(t) M (t) = K e(t) + K ∫ e(τ)dτ + K (A8) F P I t D 0 Z dt de(t) MF(t) = KPe(t) + KI e(τ)dτ + KD (A8) where e(t) = ω∗(t) − ω(t) and ω∗(t) is the demanded0 engine speed. dt where eTo(t) avoid= ω∗ abrupt(t) − ω changes(t) and ωin∗ control(t) is the variable, demanded and overshooting engine speed. in output variable, a modified I- PD scheme [29] has been used: To avoid abrupt changes in control variable, and overshooting in output variable, a modified I-PD t ( ) scheme [29] has been used: ∗ dω t MF(t) = KI ∫ (ω (τ) − ω(τ))dτ − KPω(t) − KD (A9) 0 dt Z t ∗ dω(t) Finally, responseM Ftime(t) =is KreducedI (ω introducing(τ) − ω(τ)) ad τfeedforward− KPω(t) − component.KD Because accelerator(A9) 0 dt position is not the control variable, a linear term is introduced to decouple accelerator effect from

Energies 2017, 10, 1690 16 of 17

Finally, response time is reduced introducing a feedforward component. Because accelerator positionEnergies 2017 is, not10, 1690 the control variable, a linear term is introduced to decouple accelerator effect16 from of 17 effective torque (control variable). The ﬁnal control law is shown in Equation (A10) and its blocks diagrameffective cantorque be seen (control in Figure variable). A2: The final control law is shown in Equation (A10) and its blocks diagram can be seen in Figure A2: Z t ∗ ∗ dω(t) MF(t) = Kωω (t) + KtI (ω (τ) − ω(τ))dτ − KPω(t) −dωK(Dt) − Kα(t) (A10) ∗ 0∗ dt MF(t) = Kωω (t) + KI ∫ (ω (τ) − ω(τ))dτ − KPω(t) − KD − Kα(t) (A10) 0 dt Main advantages of the described control law are the following: Main advantages of the described control law are the following: • Based on a I-PD control scheme. It is a modiﬁed PID structure but with the same characteristic  equationBased on as a I a-PD classical control one scheme. [29]. It is a modified PID structure but with the same characteristic • Integralequation action as a classical acts on errorone [29]. signal, cancelling it in steady state. • OnlyIntegral measured action acts output on error signal signal, needs cancelling to be derived. it in steady Abrupt state. changes in calculated values are  avoided,Only measured reducing output control signal and output needs variables to be derived. overshooting. Abrupt changes in calculated values are avoided, reducing control and output variables overshooting. • Reference signal prefeeding reduces system time response.  Reference signal prefeeding reduces system time response. • Decoupling term reduces accelerator signal effects in control variable.  Decoupling term reduces accelerator signal effects in control variable.

M-decoupling n-controller a

Kw K + - + Ki 1 Plant A w* + + + MF + w s K s + B - - -

1 MC K ·sign() K C

KP + KD·s

Figure A2. αα⁄ mode control law. Figure A2. ωωmode control law.

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