Model Based Control Method for Diesel Engine Combustion

energies

Article Model Based Control Method for Diesel Engine Combustion

Hu Wang *, Xin Zhong, Tianyu Ma, Zunqing Zheng and Mingfa Yao State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China; [email protected] (X.Z.); [email protected] (T.M.); [email protected] (Z.Z.); [email protected] (M.Y.) * Correspondence: [email protected]

Received: 29 September 2020; Accepted: 10 November 2020; Published: 19 November 2020

Abstract: With the increase of information processing speed, more and more engine optimization work can be processed automatically. The quick-response closed-loop control method is becoming an urgent demand for the combustion control of modern internal combustion engines. In this paper, artificial neural network (ANN) and polynomial functions are used to predict the emission and engine performance based on seven parameters extracted from the in-cylinder pressure trace information of over 3000 cases. Based on the prediction model, the optimal combustion parameters are found with two different intelligent algorithms, including genetical algorithm and fish swarm algorithm. The results show that combination of quadratic function with genetical algorithm is able to obtain the appropriate combustion control parameters. Both engine emissions and thermal efficiency can be virtually predicted in a much faster way, such that enables a promising way to achieve fast and reliable closed-loop combustion control.

Keywords: closed-loop control; diesel combustion; virtual emission prediction; artiﬁcial neural network; diesel engine

1. Introduction Most of the internal combustion engines still use a calibrated map for the control of in-cylinder combustion, which needs a lot of work. Combined with the fast development of advance combustion modes, flexible and transient control of in-cylinder combustion is becoming an urgent demand [1]. In order to realize closed-loop control of diesel combustion, the characteristics of in-cylinder combustion should be recognized, based on detected signal of combustion information. Among all the control methods for partially premixed combustion, cylinder pressure-based combustion phase control is an essential technology for diesel combustion. According to the measurement of in-cylinder pressure, real-time combustion characteristics can be derived and analyzed [2]. The injection timing was proved to be strongly related to the center of combustion phase (CA50) at an early time [3]. Thus, with multiple-pulse fuel injection, the combustion phase and heat release rate can be controlled according to the feedback of in-cylinder pressure [4]. Fang et al. [5] realized multiple combustion modes control with multiple injections based on the cylinder pressure, which could achieve flexible heat release. By changing the exhaust gas recirculation (EGR) and air/fuel ratio, CA50 and maximum pressure rise rate of gas-diesel dual-fuel combustion can also be controlled [6]. Besides this, the optimal in-cylinder combustion can be obtained by adjusting the main injection timing, which was proved by Hu et al. [7] in a dieseline fueled flexible fuel engine. Combining control of EGR and injection timing, the combustion stability can be insured according to Yao et al. [8]. More recently, Willems et al. [9] investigated cylinder pressure-based control in a heavy-duty diesel engine with EGR. The relative error of the predicted NOx emission based on cylinder pressure is on the order of 12%. Several other scholars are approaching combustion phase control through various

Energies 2020, 13, 6046; doi:10.3390/en13226046 www.mdpi.com/journal/energies Energies 2020, 13, x FOR PEER REVIEW 2 of 14 Energies 2020, 13, 6046 2 of 13 through various other methods. Thor et al. [10] estimated the combustion phase based on the measurements from the crankshaft torque sensor, which is convenient, but owing uncertainty to a otherwide methods.range of Thorworking et al. [10conditions.] estimated According the combustion to the phase reviews based above, on the measurementspressure-based from engine the crankshaftcombustion torque phase sensor, and emission which is control convenient, can butbe realized owing uncertainty by estimating to a widethe major range combustion of working conditions.parameters Accordingand predicting to the the reviews engine above, emissions pressure-based under the enginecertain combustion combustion phase parameter and emission values. controlHowever, can extracting be realized the by major estimating combustion the major paramete combustionrs which parameterscould describe and the predicting combustion the process engine emissionsis still controversial. under the certainThus, choosing combustion theparameter representa values.tive control However, variables extracting is important the major forcombustion the closed- parametersloop combustion. which could describe the combustion process is still controversial. Thus, choosing the representativeApart from control picking variables the right is important combustion for thepara closed-loopmeters, estimating combustion. the emissions and engine performanceApart from with picking these inputs the right is also combustion important. Tr parameters,aditionally, estimating the engine emissions the emissions can be and estimated engine performancebased on emission with thesemodels inputs considering is also important. chemical reac Traditionally,tions, but it the is time-consuming engine emissions work. can be Some estimated smart basedalgorithms on emission have been models applied considering to this work chemical in rece reactions,nt years. but Network it is time-consuming training was work.used by Some Junsmart et al. algorithms[11] to predict have NOx been emission applied with to this typical work incharacteristics recent years. of Network a heat release training curve. was usedAlso, by OguzJun etet al. [[12]11] toapplied predict three NOx layers emission of ANN with to typical estimate characteristics power, torque, of a heatand fuel release consumption curve. Also, of Oguza biofuel et al. diesel [12] appliedengine. The three result layers showed of ANN high to accuracy. estimate power,D’Ambrosio torque, et al. and [13] fuel developed consumption a semi-empirical of a biofuel model diesel engine.for the prediction The result of showed NOx emission high accuracy. according D’Ambrosio to the maximum et al. [13 burned] developed gas temperature, a semi-empirical air/fuel model ratio, forand the other prediction engine ofoperating NOx emission boundary according conditions. to the The maximum maximum burned temperature gas temperature, was found air/ fuelto be ratio, the andmost other important engine factor operating that boundaryaffects NOx conditions. formation. The In maximum addition, intelligent temperature algorithms was found have to be also the mostbeen importantapplied to factorengine that combustion affects NOx and formation. performance In addition, optimization. intelligent Verma algorithms et al. have[14] applied also been genetical applied toalgorithm engine combustionto search for and the performance optimal injection optimization. timing Vermaand duration, et al. [14 ]in applied order geneticalto reduce algorithm both the toemissions search forand the specific optimal fuel injection consumption. timing and Alonso duration, et al. in [15] order used to reduceboth the both ANN the emissionsand genetical and specificalgorithm fuel to consumption.optimize the engine Alonso combustion et al. [15] used and bothoperation the ANN to improve and genetical the fuel algorithm consumption to optimize at two thesteady engine state combustion conditions, and which operation proves to improveto be a pr theomising fuel consumption way for the at flexible two steady engine state combustion conditions, whichoptimization. proves to be a promising way for the flexible engine combustion optimization. Combining the the prediction prediction model model and and smart smart optimiza optimizationtion algorithm algorithm is a good is a idea good for idea the forclosed- the closed-looploop control control of combustion; of combustion; but it but does it does not notshow show large-scale large-scale impact, impact, because because of of the the limitedlimited combustion analysisanalysis data data and and its limitationits limitation on engine on engine type. In type. this paper,In this seven paper, combustion seven combustion parameters areparameters extracted, are representing extracted, represen the basicting characteristics the basic characteristics of in-cylinder of combustion.in-cylinder combustion. Predictionmodels Prediction are builtmodels and are trained built and with trained over 3000 with engine over 3000 working engine data working to study data the to relationship study the relationship between combustion between parameterscombustion and parameters engine emissions and engine and emissions performance. and Finally, performance. based on Fina thelly, prediction based on model, the prediction intelligent algorithms,model, intelligent including algorithms, genetical including algorithm genetical and fish algorithm swarm algorithm,and fish swarm are applied algorithm, for theare optimalapplied combustionfor the optimal parameter combustion in a wide parameter range of in engine a wide working range conditions. of engine Theworking basic structureconditions. of thisThe paperbasic isstructure shown inof Figurethis paper1. is shown in Figure 1.

Figure 1. Structure of this paper.

2. Description of Loop Control and Optimum Plan Owing to the complexity of turbulent combustion and transient engine engine operation operation characteristics, characteristics, the control control of of in-cylinder in-cylinder combustion combustion is always is always a challenge a challenge for both for the both sensing the sensing system systemand control and controlalgorithm. algorithm. For a given For adiesel given engine diesel with engine fixe withd combustion ﬁxed combustion chamber chamberand compression and compression ratio, the ratio,combustion the combustion control is control usually is usuallybased on based calibrated on calibrated maps. maps. In this In case, this case, the theinterpolation interpolation method method is isusually usually used used for for the the transient transient control, control, which which restricts restricts the the accuracy accuracy of of the the control control system. system. For the looploop controlcontrol method,method, thethe combustioncombustion parameters,parameters, such as CA50CA50 andand combustioncombustion duration, etc., were were usually usually derive derivedd based based on on the the in-cylinder in-cylinder pressure. pressure. The The combustion combustion duration duration is isdefined deﬁned as asCA90-CA10. CA90-CA10. CA10, CA10, CA50, CA50, and and CA90 CA90 are arethe thecrank crank angles angles with with 10%, 10%, 50%, 50%, and and90% 90%fuel

Energies 2020, 13, 6046 3 of 13 Energies 2020, 13, x FOR PEER REVIEW 3 of 14 fuelconsumption, consumption, respectively, respectively, which which are important are important parameters parameters characterizing characterizing the combustion the combustion process process[16]. Therefore, [16]. Therefore, CA50 and CA50 combustion and combustion duration duration are included are included in the in the input input parameters parameters of of the the mode. mode. TheThe pressurepressure and and temperature temperature change change during during the the combustion combustion process process are are the the key key points points of describing of describing the in-cylinderthe in-cylinder combustion, combustion, which which can be can represented be represented by peak pressure,by peak pressurepressure, rise pressure rate, max rise combustion rate, max temperature,combustion temperature, CA50, and combustion CA50, and duration combustion (Figure duration2). A mathematical (Figure 2). model A mathematical was then built model to reﬂect was thethen relationships built to reflect among the in-cylinder relationships combustion, among enginein-cylinder emission, combustion, and performance. engine emission, The emission and predictionperformance. model The (normally emission grey-box prediction model) model is shown(norma inlly Figure grey-box2. The model) engine is performance shown in Figure parameters, 2. The suchengine as thermalperformance eﬃciency, parameters, were set such as optimization as thermal ef objective.ficiency, Optimalwere set engine as optimization performance objective. can be obtainedOptimal byengine searching performance the optimal can combustion be obtained control by parameterssearching throughthe optimal an intelligent combustion optimization control algorithm.parameters Combined through an with intelligent immense optimization quantities of algorithm. engine experimental Combined data with for immense model validation quantities and of training,engine experimental optimal combustion data for parametersmodel validation can be and obtained training, to guide optimal the in-cylindercombustion combustion parameters control. can be obtained to guide the in-cylinder combustion control.

FigureFigure 2.2. Diagram of combustion looploop controlcontrol algorithm.algorithm.

3.3. Mathematical Model for Engine EmissionEmission andand PerformancePerformance PredictionsPredictions InIn thisthis part,part, twotwo methodsmethods areare introducedintroduced andand usedused forfor predictingpredicting thethe engineengine emissionsemissions andand performance.performance. FigureFigure3 shows3 shows the testthe bench test wherebench thewh experimentsere the experiments data were collected.data were The collected. experiments The wereexperiments conducted were on theconducted modified six-cylinderon the modified heavy-duty six-cylinder diesel engine. heavy-duty The sixth diesel cylinder engine. was separatedThe sixth fromcylinder the otherwas separated cylinders andfrom was the equippedother cylinders with independent and was equipped port and with directfuel independent injection port systems and (DI)directfuel to intake injection temperature systems and (DI) pressure to intake regulating temperature systems, and an pressure EGR system, regulating and sosystems, on. The an engine EGR specificationssystem, and so are on. shown The engine in Table specifications1. Major physical are show propertiesn in Table of diesel 1. Major used physical in experiments properties are of shown diesel inused Table in2 experiments. Over 3000 experimentalare shown in dataTable shown 2. Over in 300 Figure0 experimental4 were tested data in order shown to understand in Figure 4 were the engine tested performancein order to understand under various the engine operating performance conditions. under About various 2000 operating cases, marked conditions. as black About dots, 2000 were cases, used formarked model as training, black dots, while were the used other for 1000, model shown training in red, squares,while the were other kept 1000, for shown model in validation. red squares, For were each speedkept for/load model point validation. in Figure 2For, the each injection speed/load timing, point EGR, in intakeFigure pressure,2, the injection etc., aretiming, di ff erent,EGR, intake and a randompressure, choice etc., are of datadifferent, was used and fora random the model choice building of data/training. was used Seven for the parameters model building/training. representing the combustionSeven parameters characteristics representing were calculatedthe combustion based ch onaracteristics cylinder pressure, were calculated as shown inbased Figure on2 .cylinder Firstly, sensitivitypressure, as analysis shown wasin Figure performed 2. Firstly, to study sensitivity the influence analysis ofwas combustion performed parameters to study the on influence emissions of andcombustion engine thermalparameters efficiency. on emission Tables3 and shows engine the thermal sensitivity efficiency. of combustion Table 3 controlshows the parameters sensitivity on of enginecombustion performance control parameters and emissions. on engine For the performa enginence emissions, and emissions. two combustion For the engine control emissions, parameters two withcombustion the highest control sensitivity parameters are listed, with whichthe highest also greatly sensitivity affect are the listed, engine which performance. also greatly For affect example, the EGRengine and performance. the maximum For combustion example, EGR temperature and the are maximum mostly sensitive combustion to NOx temperature in this engine are mostly under disensitivefferent operatingto NOx in conditions. this engine Therefore, under different EGR can operating be an effective conditions. method Therefore, for both combustionEGR can be and an NOxeffective emission method control. for both However, combustion EGR and also NOx greatly emission affects control. CO, which However, means EGR that, also although greatly massive affects EGRCO, couldwhich restrainmeans NOx,that, although penalty in massive CO would EGR also could be observed. restrain NOx, Thus, detailedpenalty in understanding CO would also on the be relationshipobserved. Thus, between detailed engine understanding performance and on combustionthe relationship parameters between is needed. engine performance and combustion parameters is needed.

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Figure 3. Test bench. Energies 2020, 13, x FOR PEER REVIEW Figure 3. Test bench. 4 of 14 1400 Table 1. Engine speciﬁcations. Engine displacement1200 8.42 L Bore/stroke 113 mm 140 mm × Compression ratio1000 16.8 Fuel injector 8 holes, injection cone angle 148 , nozzle diameter 0.163 mm 800 ◦ Rated power/speed 243 kW/2200 rpm Rated torque/speed600 1350 Nm/(1100–1700 rpm)

Torque (Nm) 400 Table 2. Major physical properties of diesel. 200 Boiling Point Density Low Heating Value Kinetic Viscosity Molecule Formula 3 Cetane Number (◦C) 0 (g/cm @25 ◦C) (MJ/kg) (MPa s) C12–C15 190–340600 800 0.834Figure 1000 12003. Test 1400 bench 51 1600. 1800 42.6 2000 3.24 Speed (rpm) 1400Figure 4. Driving cycles of all testing data. 1200 Table 1. Engine specifications. 1000 Engine displacement 8.42 L 800 Bore/stroke 113 mm × 140 mm Compression ratio 600 16.8 ° Fuel injector (Nm) Torque 8 holes, injection cone angle 148 , nozzle diameter 0.163 mm 400 Rated power/speed 243 kW/2200 rpm Rated torque/speed 200 1350 Nm/(1100–1700 rpm) 0 Table600 2.800 Major1000 physical1200 prop1400erties1600 of diesel.1800 2000 Speed (rpm) Kinetic Molecule Boiling Density Cetane Low Heating FigureFigure 4.4. DrivingDriving cyclescycles ofof allall testingtesting data.data. Viscosity (MPa Formula Point (°C) (g/cm3@25 °C) Number Value (MJ/kg) Table 3. Sensitivity of combustion control parameters. s) Table 1. Engine specifications. C12–C15 190–340 0.834 51 42.6 3.24 Most Sensitive Combustion Variable Engine displacement 8.42 L Bore/strokeThermal eﬃciencyTable 3. Sensitivity Combustion of combustion113 duration, controlmm × CA50 140parameters. mm Soot Equivalence ratio, Combustion duration Compression ratio 16.8 NOx Most EGR, Sensitive Max combustion Combustion temperature Variable FuelThermal injectorHC efficiency 8 holes, Combustioninjection Max combustion cone duration, angle temperature, 148 CA50, nozzle Peak diameter pressure 0.163 mm RatedSootCO power /speed Equivalence Combustion ratio, duration,243 kW/ Combustion2200 EGR rpm duration RatedNOxExhaust torque/ gasspeed temperature EGR, Max Max combustion1350 combustion Nm/(110 temperature, temperature0–1700 Equivalence rpm) ratio HC Max combustion temperature, Peak pressure CO Table 2. Major Combustion physical propertiesduration, ofEGR diesel . Exhaust gas temperature Max combustion temperature, Equivalence ratioKinetic Molecule Boiling Density Cetane Low Heating Viscosity (MPa Formula Point (°C) (g/cm3@25 °C) Number Value (MJ/kg) 3.1. Engine Emission Model Based on Artificial Neural Networks (ANN) s) C12–C15 190–340 0.834 51 42.6 3.24

Table 3. Sensitivity of combustion control parameters.

Most Sensitive Combustion Variable Thermal efficiency Combustion duration, CA50 Soot Equivalence ratio, Combustion duration NOx EGR, Max combustion temperature HC Max combustion temperature, Peak pressure CO Combustion duration, EGR Exhaust gas temperature Max combustion temperature, Equivalence ratio

3.1. Engine Emission Model Based on Artificial Neural Networks (ANN)

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3.1. Engine Emission Model Based on Artiﬁcial Neural Networks (ANN)

EnergiesConsidering 2020, 13, x FOR the PEER nonlinear REVIEW properties of the relationship between combustion parameters5 of 14 and engine performance, ANN is a promising tool to predict engine emissions and thermal efficiency. The typicalConsidering combustion the nonlinear parameters properties are obtained of the re basedlationship onthe between in-cylinder combustion pressure. parameters In this and paper, a 10-layerengine performance, ANN, which ANN contains is a promising nine neurons tool to in predict every hiddenengine emissions layer, is implementedand thermal efficiency. and trained withThe engine typical test combustion data in MATLAB. parameters Figure are obtained5 shows thebased prediction on the in-cylinder results with pressure. ANN In method, this paper, in which a the x-coordinate10-layer ANN, ofwhich cycle contai symbolsns nine represents neurons in experimental every hidden value,layer, is while implemented the y-coordinate and trained represents with engine test data in MATLAB. Figure 5 shows the prediction results with ANN method, in which the the predicted value. According to the difference between cycle dispersion and perfect fitting line x-coordinate of cycle symbols represents experimental value, while the y-coordinate represents the (y = x), it can be seen that the 10-layer ANN is able to predict the NOx and engine thermal efficiency predicted value. According to the difference between cycle dispersion and perfect fitting line (y = x), accurately.it can be However, seen that thethemodel 10-layer prediction ANN is isable less toaccurate predict the for sootNOx andand COengine emissions, thermalowing efficiency to their highlyaccurately. non-linear However, properties. the model In addition,predictionother is less factors,accuratesuch for soot as sprayand CO and emissions, flow characteristics owing to their and combustionhighly non-linear chamber properties. geometry, wouldIn addition, also aotherffect thefactors, engine such emission as spray and and performance. flow characteristics This increases and the uncertaintycombustion ofchamber ANN predictiongeometry, results.would also affect the engine emission and performance. This increases the uncertainty of ANN prediction results.

(a) (b)

(e) (f)

Figure 5. (a) NOx (b) Soot (c) HC (d) CO (e) Exhaust gas temperature (f) Thermal efficiency. Results Figure 5. (a) NOx (b) Soot (c) HC (d) CO (e) Exhaust gas temperature (f) Thermal eﬃciency. Results of of prediction based on artificial neural network (ANN). prediction based on artiﬁcial neural network (ANN). 3.2. Engine Emission Model Based on Polynomial Functions

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3.2. Engine Emission Model Based on Polynomial Functions Increasing the layers of ANN should be able to improve the prediction accuracy, however, longer calculation time is also needed, which makes it diﬃcult to meet the quick response requirement within the control system. Another way to correlate the combustion parameters with engine performance is to build functions using a known type, such as the equation shown in Equation (1). ! ! dp Y = f n, Torque, ∅, EGR, Tmax, , pmax, CA50, Combs_Duration (1) dϕ max in which Y represents the engine outputs, such as HC, NOx emissions, etc. The input variables are the basic operating conditions and combustion parameters described in Figure2. Therefore, the output can be calculated by the nine variables with a speciﬁc formula, f. In this part, quadratic polynomials are applied, as presented in Equation (2).

Xn Xn y = a0 + aixi + aijxixj (2) i=1 i, j = 1 i j ≤ With nine inputs, there would be 55 terms at most in Equation (2), considering the coupling effect of every two combustion parameters. A total of 2000 stable engine running cases were used to determine the fitting coefficients in Equation (2). This was conducted with MATLAB Mode-Based Calibration tools, and the model setup is shown in Figure6. Trained by these stable engine operating data, detailed formulation of Equation (2), which could calculate emissions and engine performances, can be obtained. Equation (3) shows the equation for thermal efficiency calculation, and the prediction results are illustrated in Figure7. It is found that the quadratic function could also provide reasonable prediction results under most operating conditions. Different from the ANN method, the effect of each combustion parameter can be analyzed with these quadratic functions directly by its partial derivatives. This means that this method is able to help making macroscopic optimization by showing the slope change of engine performance.

Thermal_eﬃciency = ( 77.63 + 2.068*CA50 + 1.38*Combst_Duration 115.34*Phi + − − 0.099*T_max + 0.019*n 0.0166*CA50ˆ2 0.0233*CA50*Combst_Duration + − − 3.016*CA50*EGR + 0.012*CA50*P_peak 0.063*CA50*P_rate 0.000642*CA50*T_max − − 0.00076*CA50*Torque 0.000308*CA50*n 0.0195*Combst_Durationˆ2 + − − − 0.569*Combst_Duration*Phi 0.000451*Combst_Duration*T_max + − 0.000465*Combst_Duration*Torque + 0.000267* Combst_Duration*n 21.427*EGRˆ2 + − (3) 1.489*EGR*P_peak + 104.67*EGR*Phi 0.0269*EGR*T_max 0.149*EGR*Torque − − − 0.0406*EGR*n 1.055*P_peak*Phi + 0.000252*P_peak*T_max + 8.42e- − 05*P_peak*Torque 0.000156* P_peak*n +1.869*P_rate*Phi 0.00103*P_rate* T_max + − − 0.0013*P_rate*Torque + 0.000832*P_rate*n 0.0101*Phi* T_max + 0.105*Phi*Torque + − 0.0518*Phi*n 3.378e-5*T_max*Torque 2.38e-05*T_max*n 8.66e-06* Torqueˆ2 + − − − 1.016e-5*Torque*n)/100; Energies 2020, 13, 6046 7 of 13 EnergiesEnergies 20202020,, 1313,, xx FORFOR PEERPEER REVIEWREVIEW 77 ofof 1414

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FigureFigure 6.6. ModelModel setup setup in in Mode-Based Mode-Based CalibrationCalibration tools.tools.

Figure 6. Model setup in Mode-Based Calibration tools.

FigureFigure 7. 7. ResultsResults of of prediction prediction of of virtual virtual thermal thermal efficiency eefficiencyﬃciency basedbased onon quadraticquadratic function.function.

InIn orderorder Figure toto comparecompare 7. Results thethe of prediction predictivepredictive of virtual capabilitycapability thermal of ofefficiency these these twobased two methods,methods,on quadratic another function. group group of data, coveringcovering different didifferentﬀerent andand widerwider rangerange ofof operatingoperating conditions,conditions, were were usedused forfor thethe testtest andand validationvalidation In order to compare the predictive capability of these two methods, another group of data, (Figure(Figure4 4;4;; red redred squares). squares).squares). As AsAs shown shownshown in inin Figure FigureFigure8 8,,8, the ththee red redred marks marksmarks represent representrepresent the thethe results resultsresults of ofof the thethe ANN ANNANN method, method,method, covering different and wider range of operating conditions, were used for the test and validation whilewhile thethe cyancyan marksmarks areare thethe resultsresults predictedpredicted byby thethe quadraticquadratic function.function. InIn general,general, bothboth methodsmethods while(Figure the cyan 4; red marks squares). arethe As shown results in predicted Figure 8, bythe thered quadraticmarks represent function. the results In general, of the bothANN methods method, show showreasonableshowwhile reasonablereasonable the prediction cyan predictionmarksprediction results are the forresultsresults results emissions forfor predicted emissionsemissions and performance by the andand quadratic performanceperformance at most function. conditions. atat In mostmost general, conditions. However,conditions. both methods combustion However,However, productscombustioncombustionshow andreasonable productsproducts emissions prediction andand with emissionsemissions highly results non-linear for withwith emissions highlyhighly characteristics and nono performancen-linearn-linear are stillcharacteristicscharacteristics at di mostﬃcult conditions. to estimate,areare stillstill However, fordifficultdifficult example, toto estimate,soot.estimate,combustion Besides forfor example,example, this, products ANN soot.soot. tendsand BesidesBesidesemissions to show this,this, with larger ANNANN highly random tendtend noss n-linear to errorsto showshow forcharacteristics largerlarger high valuerandomrandom are results, errorserrorsstill whiledifficult forfor highhigh quadratic to valuevalue results,functionresults,estimate, whilewhile tends for quadraticquadratic to example, underestimate functionfunctionsoot. Besides the tendstends high this, toto value. ANN underesunderes tend Comparingtimatetimates to show thethe the largerhighhigh prediction value.value.random ComparingComparing errors results for of high NOx thethe value predictionprediction and other resultsincompleteresultsresults, ofof NOx NOxwhile combustion and andquadratic otherother products function incompleteincomplete by tends quadratic combustion combustionto underes functiontimate productsproducts and the ANN, high byby value. thequadraticquadratic prediction Comparing functionfunction accuracy the predictionandand of ANN,ANN, quadratic thethe predictionfunctionpredictionresults is accuracyof similaraccuracy NOx toand ofof that quadraticotherquadratic of ANN.incomplete functionfunction combustion isis similarsimilar products toto thatthat ofof by ANN.ANN. quadratic function and ANN, the prediction accuracy of quadratic function is similar to that of ANN.

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Figure 8. Cont.

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(d) (e) (f)

(g) (h) (i)

(j) (k) (l)

FigureFigure 8. (a 8.) NOx(a) NOx (b) ( Sootb) Soot (c )(c HC) HC (d ()d) NOx NOx ((ee)) SootSoot ( ff)) HC HC (g (g) )CO CO (h ()h Thermal) Thermal efficiency eﬃciency (i) Exhaust (i) Exhaust gas temperaturegas temperature (j) CO(j) CO (k) (k Thermal) Thermal e ﬃefficiencyciency ((ll)) ExhaustExhaust gas gas temperature. temperature. Comparison Comparison of prediction of prediction abilityability between between ANN ANN and and polynomial polynomial function. function.

4. Intelligent4. Intelligent Optimization Optimization Algorithms Algorithms for for CombustionCombustion Control Control Parameters Parameters The relationshipThe relationship between between engine engine performance performance and combustion combustion parameters parameters can can be found be found to guide to guide the development of optimal combustion control strategy. In this part, the genetic algorithm and fish the development of optimal combustion control strategy. In this part, the genetic algorithm and fish swarm algorithm are used to find the optimal combustion parameters. The estimated thermal swarm algorithm are used to find the optimal combustion parameters. The estimated thermal efficiency efficiency by the prediction model, which is called virtual thermal efficiency in the following parts, by theis predictionregarded as model, the optimization which is calledobjective. virtual Consider thermaling the effi conversionciency in the efficiency following of aftertreatment parts, is regarded by as the optimizationestimating the objective. exhaust temperature, Considering the the final conversion emissions e fficanciency also be of aftertreatmentpredicted. The engine by estimating virtual the exhaustthermal temperature, efficiency the will final be emissionsset as 10%, can as alsolong beas predicted.the emission The exceeds engine the virtual regulation thermal limits. efficiency The will be setcomputing as 10%, as process long as of the the emission final virtual exceeds thermal the regulation efficiency limits. is plotted Thecomputing in Figure process9. During of the final virtualoptimization thermal e ffiprocess,ciency quad is plottedratic function in Figure is applied9. During to calculate the optimization the engine emission process, and quadratic performance. function is appliedThe variation to calculate ranges the of enginecombustion emission control and para performance.meters are shown The variationin Table 4 ranges by analyzing of combustion the controlavailable parameters engine are experimental, shown in Tableand CA504 by analyzingis calculated the by available the prediction engine model, experimental, based on the and given CA50 is range of combustion parameters. calculated by the prediction model, based on the given range of combustion parameters.

Table 4. The variation range of combustion control parameters.

Range of Variation Equivalence ratio [0.28, 1.03] EGR [0.004, 0.5] Max combustion temperature (K) [1350, 2700] Peak pressure (MPa) [5, 17.3] Pressure rise rate (MPa/CA) [0.19, 1.18] Combustion duration (CA) [6.5, 39.5] Energies 2020, 13, 6046 9 of 13 Energies 2020, 13, x FOR PEER REVIEW 9 of 14

Figure 9. Framework of virtual thermal eefficiencyﬃciency calculation.calculation.

Table 4. The variation range of combustion control parameters. Firstly, by applying genetic algorithm, the optimal combustion parameters were searched at 1400 r/min and 945 Nm condition, as shown in Figure 10. TheRange maximum of Variation virtual thermal efficiency was obtained after about 50Equivalence iterations, and ratio the final optimal combustion [0.28, 1.03] parameters are listed in Table5. Theoretically, the engineEGR thermal efficiency could reach the optimized[0.004, 0.5] value, as long as the combustion parameters are controlledMax to combustion be close to the temperature calculated value.(K) [1350, In order 2700] to achieve optimal virtual thermal efficiency, the maximumPeak in-cylinder pressure (MPa) combustion temperature [5, 17.3] needs to reach 2234 K, and the peak pressure should reachPressure 145.76 bar. rise However, rate (MPa/CA) these two combustion [0.19, 1.18] parameters are strongly related to the in-cylinder flowCombustion and spray combustion duration (CA) process, and [6.5, these 39.5] processes are not only controlled by the overall equivalence ratio and fuel heat value, but also the turbulent intensity and the flame pulsation.Firstly, This by meansapplying that genetic these two algorithm, combustion the parametersoptimal combustion should be revisedparameters and updated,were searched based onat the1400 experimental r/min and 945 in-cylinder Nm condition, pressure as shown in the realin Figure transient 10. loopThe maximum combustion virtual control, thermal to improve efficiency the predictingwas obtained accuracy after ofabout the mathematical50 iterations, model.and the final optimal combustion parameters are listed in TableEnergies 5. 2020 Theoretically,, 13, x FOR PEER the REVIEW engine thermal efficiency could reach the optimized value, as long as10 ofthe 14 combustion parameters are controlled to be close to the calculated value. In order to achieve optimal virtual thermal efficiency, the maximum in-cylinder combustion temperature needs to reach 2234 K, and the peak pressure should reach 145.76 bar. However, these two combustion parameters are strongly related to the in-cylinder flow and spray combustion process, and these processes are not only controlled by the overall equivalence ratio and fuel heat value, but also the turbulent intensity and the flame pulsation. This means that these two combustion parameters should be revised and updated, based on the experimental in-cylinder pressure in the real transient loop combustion control, to improve the predicting accuracy of the mathematical model.

FigureFigure 10. 10.Optimizing Optimizing processprocess of of genetic genetic algorithm. algorithm.

Table 5. Optimized combustion control parameters.

Maximum Peak Pressure Combustion CA50 Equivalence Combustion EGR Pressure Rise Rate Duration (°CA Ratio Temperature (bar) (bar/°CA) (°CA) ATDC) (K) 0.65 0.3655 2234.2 145.76 8.05 28.53 8.75

Figure 11 shows the optimized results at all these operating conditions, covering engine speed from 1000 r/min to 1600 r/min and torque from 600 Nm to 1300 Nm. It can be seen in Figure 11a that overall lean combustion is beneficial to thermal efficiency improvement, mainly due to the higher specific heat ratio with air dilution. In addition, heat losses could also be reduced with increased heat capacity, owing to more fresh air. Figure 11b,c show the distribution of optimized combustion phase and EGR, respectively. It indicates that an early combustion with relatively higher EGR could be helpful for thermal efficiency improvement at low speed and low load conditions, while EGR rate should be reduced with retarded combustion phase under high speed and high load conditions. By adjusting these combustion control parameters, improvement in virtual thermal efficiency can be obtained, as shown in Figure 11d.

(a) (b)

Figure 11. (a) Phi (b) CA50 (c) EGR (d) Virtual thermal efficiency. Optimizing results based on the genetic algorithm.

Energies 2020, 13, x FOR PEER REVIEW 10 of 14

Figure 10. Optimizing process of genetic algorithm. Energies 2020, 13, 6046 10 of 13 Table 5. Optimized combustion control parameters.

TableMaximum 5. Optimized combustion control parameters. Peak Pressure Combustion CA50 Equivalence Combustion Equivalence Maximum Combustion Peak Pressure Pressure Rise Combustion CA50 EGREGR Pressure Rise Rate Duration (°CA RatioRatio TemperatureTemperature (K) (bar) Rate (bar/ CA) Duration ( CA) ( CA ATDC) (bar) (bar/°CA)◦ (°CA)◦ ◦ ATDC) 0.65 0.3655 2234.2(K) 145.76 8.05 28.53 8.75 0.65 0.3655 2234.2 145.76 8.05 28.53 8.75 Figure 11 shows the optimized results at all these operating conditions, covering engine speed fromFigure 1000 r/ min11 shows to 1600 the r/ minoptimized and torque results from at all 600 thes Nme tooperating 1300 Nm. conditions, It can be seencovering in Figure engine 11 aspeed that overallfrom 1000 lean r/min combustion to 1600 r/min is beneficial and torque to thermal from 600 effi Nmciency to 1300 improvement, Nm. It can mainlybe seen duein Figure to the 11a higher that specificoverall lean heat combustion ratio with air is dilution.beneficial Into addition,thermal efficiency heat losses improvement, could also be mainly reduced due with to the increased higher heatspecific capacity, heat ratio owing with to air more dilution. fresh air.In addition, Figure 11 heb,cat showlosses the could distribution also be reduced of optimized with increased combustion heat phasecapacity, and owing EGR, respectively.to more fresh Itair. indicates Figure 11b,c that an sh earlyow the combustion distribution with of op relativelytimized highercombustion EGR couldphase beand helpful EGR, respectively. for thermal eItffi ciencyindicates improvement that an early at combustion low speed andwith low relatively load conditions, higher EGR while could EGR be ratehelpful should for thermal be reduced efficiency with retarded improvement combustion at low phase speed under and highlow load speed conditions, and high loadwhile conditions. EGR rate Byshould adjusting be reduced thesecombustion with retarded control combustion parameters, phase improvement under high speed in virtual and thermalhigh load effi conditions.ciency can beBy obtained,adjusting asthese shown combustion in Figure control11d. parameters, improvement in virtual thermal efficiency can be obtained, as shown in Figure 11d.

(a) (b)

FigureFigure 11.11. ((a)) PhiPhi ((bb)) CA50CA50 ((cc)) EGREGR ((dd)) VirtualVirtual thermalthermal eefficiency.ﬃciency. OptimizingOptimizing resultsresults basedbased onon thethe geneticgenetic algorithm.algorithm.

The fish swarm algorithm was also used to study the influence of algorithm on the final optimal prediction results. Figure 12 shows the optimal result with fish swarm algorithm under the same operating conditions (1400 r/min, 945 Nm). For this single case, the fish swarm algorithm took about 1 min to find a higher target value. The optimized combustion parameters are listed in Table6. It is seen that major differences exist in peak pressure and maximum combustion temperature between these two algorithms, which also affects the pressure rise rate. These combustion parameters also need to be updated with real-time in-cylinder pressure measurement. Considering the optimization process, the genetical algorithm would give more stable results, owing to its fast searching capability for local optimum. Energies 2020, 13, x FOR PEER REVIEW 11 of 14

The fish swarm algorithm was also used to study the influence of algorithm on the final optimal prediction results. Figure 12 shows the optimal result with fish swarm algorithm under the same operating conditions (1400 r/min, 945 Nm). For this single case, the fish swarm algorithm took about 1 min to find a higher target value. The optimized combustion parameters are listed in Table 6. It is seen that major differences exist in peak pressure and maximum combustion temperature between these two algorithms, which also affects the pressure rise rate. These combustion parameters also need to be updated with real-time in-cylinder pressure measurement. Considering the optimization Energiesprocess,2020 the, 13 ,genetical 6046 algorithm would give more stable results, owing to its fast searching capability11 of 13 for local optimum.

FigureFigure 12. 12.Optimizing Optimizing process process of of ﬁsh fish swarm swarm algorithm. algorithm.

TableTable 6. 6.Optimized Optimized combustion combustion parameter. parameter. Equivalence Maximum Combustion Peak Pressure Pressure Rise Combustion CA50 EGR Maximum Peak Pressure Combustion CA50 EquivalenceRatio Temperature (K) (bar) Rate (bar/◦CA) Duration (◦CA) (◦CA ATDC) EGR Combustion Pressure Rise Rate Duration (°CA 0.49Ratio 0.363 2068.2 167.86 11.53 22.04 8.84 Temperature (K) (bar) (bar/°CA) (°CA) ATDC) Figure0.49 13 shows0.363 the calculated 2068.2 optimal results 167.86 with fish swarm 11.53 algorithm. 22.04 Again, it is observed 8.84 that overall lean combustion is beneficial for thermal efficiency improvement, as shown in Figure 13a. However,Figure one 13 noticeable shows the di calculatedfference is thatoptimal the resultresults with with fish fish swarm swarm algorithm algorithm. indicating Again, it higher is observed EGR shouldthat overall be adopted lean combustion only at low is load beneficial conditions for th (Figureermal 13efficiencyc). With improvement, the increase of as engine shown speed in Figure and load,13a. theHowever, combustion one noticeable phase advances difference at some is that cases the result while with retards fish at swarm other cases algorithm (Figure indicating 13b). This higher can beEGR explained should bybe theadopted random only e ffatect low of load combustion conditions control (Figure parameters 13c). With and the the increase characteristics of engine of speed fish swarmand load, algorithm. the combustion On the onephase hand, advances as a complex at some multiobjectivecases while retards optimization at other cases problem, (Figure the 13b). ideally This globalcan be optimal explained combustion by the random parameters effect might of combusti not exist.on Acontrol small parameters error in peak and pressure the characteristics could lead to of completelyfish swarm di algorithm.fferent optimization On the one direction. hand, as On a thecomplex other hand,multiobjective excessively optimization searching for problem, the global the optimalideally combustionglobal optimal parameters combustion would parameters result in might unrealistic not exist. value. A small error in peak pressure could leadEnergies to completely 2020, 13, x FOR different PEER REVIEW optimization direction. On the other hand, excessively searching12 of 14for the global optimal combustion parameters would result in unrealistic value. Comparing the results in Figures 11d and 13d, it is seen that the genetic algorithm performs better than the fish swarm algorithm in searching for optimal combustion control parameters. With reasonable amendment, an emission prediction model coupled with a genetic algorithm can be an option for combustion loop control.

(a) (b)

FigureFigure 13. 13.(a )(a Phi) Phi ( (bb)) CA50 (c ()c )EGR EGR (d) ( Virtuald) Virtual thermal thermal efficiency. eﬃciency. Optimal Optimal results based results on fish based swarm on ﬁsh swarmalgorithm algorithm..

5. Conclusions The innovation of this paper is the introduction of the ANN and AI method to establish the prediction model of engine emissions and performance through combustion characteristic parameters, and the performance and emission of engine are optimized under different operating conditions. Finally, combined with some transient experimental data, the control scheme of the model in the actual engine is explored, and the corresponding control strategy is proposed, which has high value in practice. Several major conclusions can be drawn, as follows: (1) In order to construct a feasible model for combustion control, seven major combustion parameters are extracted, to represent the combustion characteristics of a diesel engine, based on measured in-cylinder pressure. It is seen that both the ANN and quadratic functions can reasonably well reproduce the engine performance and emissions over wide operating conditions. However, more effort should be expended on the prediction of non-linear emissions, such as soot. (2) CA50 is the main combustion parameter which defines the combustion heat release center. Compared to the traditional ANN method, optimal CA50 can be firstly determined with quadratic functions, and the other combustion parameters can be optimized with AI, which proved to be a more realistic way for model-based combustion control. Based on this rule, a multiobjective function is proposed to optimize engine emissions and thermal efficiency. (3) Two intelligent optimization algorithms are applied to search the optimal combustion parameters. The genetical algorithm shows more stable results and faster convergence than the fish swarm algorithm. With AI, the optimal combustion parameters can be identified and estimated much faster, and the results can be further used for the adjusting of combustion strategy. Further research exists in achieving the optimal combustion process by adjusting combustion boundary conditions. In addition, more transient data will be used to assess the capability and stability of this combined loop control method.

Energies 2020, 13, 6046 12 of 13

Comparing the results in Figures 11d and 13d, it is seen that the genetic algorithm performs better than the ﬁsh swarm algorithm in searching for optimal combustion control parameters. With reasonable amendment, an emission prediction model coupled with a genetic algorithm can be an option for combustion loop control.

(1) In order to construct a feasible model for combustion control, seven major combustion parameters are extracted, to represent the combustion characteristics of a diesel engine, based on measured in-cylinder pressure. It is seen that both the ANN and quadratic functions can reasonably well reproduce the engine performance and emissions over wide operating conditions. However, more effort should be expended on the prediction of non-linear emissions, such as soot. (2) CA50 is the main combustion parameter which defines the combustion heat release center. Compared to the traditional ANN method, optimal CA50 can be firstly determined with quadratic functions, and the other combustion parameters can be optimized with AI, which proved to be a more realistic way for model-based combustion control. Based on this rule, a multiobjective function is proposed to optimize engine emissions and thermal efficiency. (3) Two intelligent optimization algorithms are applied to search the optimal combustion parameters. The genetical algorithm shows more stable results and faster convergence than the fish swarm algorithm. With AI, the optimal combustion parameters can be identified and estimated much faster, and the results can be further used for the adjusting of combustion strategy.

Further research exists in achieving the optimal combustion process by adjusting combustion boundary conditions. In addition, more transient data will be used to assess the capability and stability of this combined loop control method.

Author Contributions: Conceptualization, H.W. and T.M.; methodology, T.M.; software, T.M.; validation, X.Z. and T.M.; formal analysis, Z.Z.; investigation, H.W., X.Z. and T.M.; resources, Z.Z.; writing—original draft preparation, T.M.; writing—review and editing, X.Z.; supervision, M.Y. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the foundation of National Key R&D Program of China (2017YFB0103501) and the National Natural Science Fund of China (NSFC) through the Projects of 51876140. Conﬂicts of Interest: The authors declare that there are no conﬂict of interest.

Nomenclature

ANN Artificial neural network P_rate Pressure rise rate Combustion phasing defined by the CA50 Crank Angle of 50% accumulative Phi Equivalence ratio heat release EGR Exhaust gas recirculation T_max Max combustion temperature Combs-Duration Combustion duration outEff Indicated thermal efficiency pmax/P_peak Peak pressure PM Particulate matter DI Direct injection Energies 2020, 13, 6046 13 of 13

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