INTRODUCTIONCurrent trends in internal combustion engine design

defined by new stringent fuel economy and emissionstandards clearly indicate the necessity of expanding engineoperating conditions into areas where risk of abnormalcombustion is higher, and therefore precise monitoring ofcombustion and feedback control becomes paramount forsafe, efficient engine operation. Cycle-to-cycle and cylinder-to-cylinder combustion variation due to the stochastic natureof the flame initiation and propagation process create anopportunity to improve engine control by monitoring the in-cylinder combustion in real time. Combustion systemsemploying high levels of boost and/or dilution areparticularly benefitted by a combustion feedback system dueto the additional dynamic range of operation. Operationcloser to maximum brake torque (MBT), improved operationaway from MBT (when desired), improved efficiency, andreduced emissions can be enabled by real-time combustionfeedback.

An in-cylinder pressure sensor would create the bestscenario when constructing pressure-based controlalgorithms. However, this solution has not been widelyadopted since historically the available in-cylinder pressuresensors have been costly and have had limited demonstrationof reliability for mass production. Also, the pressure sensorsignal is traditionally processed by a Combustion AnalysisSystem (CAS), a laboratory-grade instrument which is notconducive to integration with the vehicle engine controlmodule (ECM).

A substantial change in the status quo of in-cylinderpressure sensor data processing techniques was introduced afew years ago with the release of the Delphi CylinderPressure Development Controller (CPDC) [1]. Despite thefact that a mass production engine delivers only limitedresolution on crank angle position, the CPDC allowedcylinder pressure sensor analysis aligned with higherresolution of the engine crankshaft position by synthesizing a

2013-01-0354Published 04/08/2013

Copyright © 2013 SAE Internationaldoi:10.4271/2013-01-0354

saeeng.saejournals.org

Ion-Sense-Based Real-Time Combustion Sensing for ClosedLoop Engine Control

Gerard Malaczynski, Gregory Roth and Donald JohnsonDelphi Automotive Systems, LLC

ABSTRACTThe cycle-to-cycle and cylinder-to-cylinder variations that occur in a spark ignited engine create the opportunity for

monitoring combustion in real time to provide useful benefits for engine control. Reduction of variation and operation ofthe engine at closer-to-optimum conditions is possible if real time feedback of the combustion process is available.

An in-cylinder pressure sensor with pressure-based control algorithms is one method of monitoring the combustionprocess. However, such a solution presents new challenges of an additional cylinder penetration location, sensor packagingand added cost. A substitute for the in-cylinder pressure sensor is a device which measures the flame conductivity,commonly known as an ionization current sensor. It can be integrated with the spark plug in the case of SI engines, or withthe glow plug in the case of compression ignition engines.

This paper presents a methodology of data acquisition and processing which leads to the extraction, in real time, of thefollowing parameters critical for closed loop engine control: combustion phasing, knock detection and combustionstability. The algorithms utilize a combination of various methods including crank-angle-aligned data extraction, digitalfiltering of the ion sense signal and neural-network-based ion signal interpretation. A Delphi Ion Sense DevelopmentController (ISDC) is used for high-speed data acquisition and subsequent real time combustion parameter computation.

Our paper demonstrates key features and reports the performance of ionization-current-based combustion sensing usingengine data. Key combustion parameters, generated by the ISDC on a combustion event basis, are sent to the enginecontroller via a Controller Area Network (CAN) for closed loop individual-cylinder combustion control.

CITATION: Malaczynski, G., Roth, G. and Johnson, D., "Ion-Sense-Based Real-Time Combustion Sensing for Closed LoopEngine Control," SAE Int. J. Engines 6(1):2013, doi:10.4271/2013-01-0354.

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0.1 degree sampling angle from the standard crankshaft-mounted target wheel.

A substitute for the in-cylinder pressure sensor in themeantime is a device which measures the flame conductivity,commonly known as an ionization current sensor. In fact, theionization current signal (called commonly ion sense) isdirectly linked to the progress of the combustion as the flametemperature and pressure together with the chemistry offlame translate into the flame's electrical conductivity. Thisproperty is typically measured starting at the onset ofcombustion and concluding close to the end of the powerstroke when the flame is extinguished (and the combustionproducts become effectively electrically non-conductive).The frame of reference, which is crank-angle-based, forconductivity monitoring is very similar to that used whenmonitoring in-cylinder pressure. Consequently, the CPDCcan be software-tuned to be utilized for high-speed ionizationcurrent data acquisition and subsequent real time combustionparameter computation by ion sense-based combustionalgorithms. In short, the CPDC controller was converted to anIon Sense Development Controller (ISDC).

The ionization current sensor itself can be integrated withthe spark plug in the case of SI engines, or with the glow plugin the case of compression ignition engines. The ion-sense-ready ignition coil circuit serving SI engines is sketched inFig. 1 and 2, and the typical signal generated by the sensor'selectronics used in the work described here is shown in Fig.3.

Fig. 1. Bias voltage (Vz) applied to the spark pluggenerates “ionization current” which is an indication of

flame conductivity.

The ionization current sensing technique has beenresearched for decades (see e.g. [2, 3, 4]) and suggested as avirtual pressure sensor in the context of pressure-basedcombustion engine control systems. So far, however, theapproach was to demonstrate the ability of the ionizationcurrent signal to deliver some combustion-related parametersby extensive post-processing of the signal. As in the case ofthe pressure signal, the ability to extract the information is

closely related to the sampling resolution and was thushistorically limited to laboratory or engine dynamometerexperiments.

Reported combustion features extracted through variousmeans from the ionization signal are: location of peakpressure (LPP) [5,6], misfire detection, knock detection [7],heat release/mass fraction of fuel burned phasing estimation[8], start of combustion [9], air-to-fuel ratio [10,11], andengine stability [12]. Since the ionization signal waveformdata interpretation are of a statistical nature, except whendetecting engine knock, the ion sense-based combustiondiagnostic represents a data-based (stochastic) approach.

Fig. 2. Simplified electronics of the ionization currentsensor and its interface with the ISDC

Fig. 3. Example of an ionization current waveform. Theflag preceding ionization current signal masks coil

ringing. The spike before the flag represents parasiticnoise acquired at the onset of ignition coil dwell, and

rising slope of the flag coincides with the sparkdischarge, thus delivering information on spark advance

(SA).

IONIZATION CURRENTINTEGRATED COILS

For development purposes, a custom ignition coil designwas produced that supports ion sense integrated electronics

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and features a high energy, high secondary current coil outputto compliment operation of a highly boosted and dilutecombustion system. The coil is capable of 100 mJ energy andover 200 mA of secondary current while delivering over 35kV of available voltage. The coils were designed as a remote-mount style for ease of packaging on the development engine.Custom high-voltage ignition cables connected the remote-mount coils to the spark plugs. An integrated coil drivecircuit using an insulated-gate bipolar transistor (IGBT) wasincluded inside the coils for ease of spark control. A photo ofthe coil is shown in Fig. 4.

Fig. 4. Remote mount, high energy, Ionization Currentenabled ignition coil.

The ion sense electronic circuit is incorporated into thetop portion of the coil and is internally connected to thesecondary output terminal. The design contains anapplication-specific integrated circuit (ASIC) that controls theionization sensing function. It also delivers the ignition coilspark control. The ASIC and associated circuitry allows foradjustable bias voltage, ionization current signal gain, signaloffset, adjustable ring-out timer and selectable flag functions.Available inter- coil flags provide the ability to multiplex theion signal from several coils onto one signal line. The circuitalso provides coil protection from over-dwell and over-current conditions. All listed features help to combat noiseand assist with interpretation of the signal by the combustionanalysis controller. One of the difficulties in interfacing theionization current signal with a separate controller is groundshift and ground noise. The intention of the design was tominimize those effects. This is achieved by an electronicarchitecture which delivers a current source type output,thereby minimizing grounding effects. Consequently, a loadresistor is required in the receiving controller (ISDC) toconvert the current signal to a voltage for subsequentprocessing by the fast analog-to-digital (A/D) converters. If avariable (or selectable) load resistor is used in the receivingcontroller, a real time automatic gain control function caneasily be implemented.

COMBUSTION PARAMETEREXTRACTION ALGORITHMS

Ion sensing methods have been used previously fordetection of misfire and knock in SI engines. Several newapproaches to combustion parameter extraction algorithmswere investigated and are presented in this paper. Inparticular, our efforts were focused on vehicle-realizableapproaches to improved knock detection, determination ofcombustion phasing and estimation of combustion stabilitydiagnostics. These approaches include the use of variousdigital signal processing techniques and the use of data-intensive artificial neural networks. The methods aredescribed in more detail in the following sections.

Improved Knock Detection AlgorithmAn abnormal combustion reveals itself in many ways.

Knock results from spontaneous ignition ahead of thepropagating flame and signals itself as an acoustic noisetransmitted through the engine structure while modulatingboth the pressure signal and the ion sense signal traces (seeFig. 5). The knock frequency depends on combustionchamber geometry and varies little with engine rotationalspeed.

Fig. 5. Knock-generated acoustic wave modulatespressure and two ionization current signals acquiredfrom the same combustion cavity. The depicted case

represents light knock.

Previous ion sense knock algorithms utilized analogfilters, whereas the techniques described in this paper utilizedigital filters to provide additional flexibility. Since theCPDC/ISDC offers sampling rates which are constant in theangular domain, the time distance between consecutivesamples acquired at constant angular distance varies withengine speed. Consequently it creates a challenge whendesigning a digital filter responsible for separating knockfrom either pressure or ionization current signal. In fact, theband-pass filter coefficients having central frequency equal tothe knock frequency are not constant but must be enginespeed dependent.

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It is worthwhile to note that the sampling rate of 0.1degree secures a sampling frequency which is higher thanminimum required by the Nyquist Theorem to reconstruct(detect) the engine knock signal. Indeed at idling speed (600rpm), sampling at 0.1 degree angular intervals translates tosampling frequency of 36 kHz, enough to detect knock of anytypical automotive-sized engine (typically knock frequency isin the range of 4 to 16 kHz). The sampling frequencyincreases with engine speed and reaches 360 kHz at a typicalmaximum speed of 6000 rpm. Sampling at idle defines theupper limit of knock frequencies which can be detected whensampling at constant steps in angular domain. In addition,digital signal processing on the frequency scale requires anti-aliasing measures. To secure extraction of the knock signaleither from pressure or ion sense signal, an anti-aliasinganalog filter, with cut off frequency lower than 18 kHz wouldbe required under assumption of sampling the signal with 0.1degree steps.

The instrumentation-grade CAS system offers a knockdetection feature utilizing a wide band-pass filter with aselectable cut-off frequency at the high end. It separates thenear-DC component of the pressure signal from its highfrequency knock-bearing component. In short, the knocksignal separation feature is not engine-specific, and thefiltering formula used assumes a low level of background(no-knock) related noise. Since the CPDC/ISDC requiresengine-specific calibration, and the knock frequency of aspecific type of engine can be measured or estimated, it isrelatively easy to upgrade the above described knockdetection filtering routine by employing narrow band-passfilter instead of a wide filter. This solution offers immunity tobackground noise and makes the algorithm mass-productionworthy, but requires engine specific calibration.

As previously described, the CPDC/ISDC system operatesin the crank-angle sampling domain. This complicates thedigital filter design, which requires constant sampling rate inthe time domain. The digital filter coefficients can becalculated for any single engine speed, which represents aconstant sampling frequency in time domain. Then, a one-dimensional calibration table linking instantaneous enginespeed to the set of coefficients representing a specific knockfrequency can be generated and used in real time to fine-tunethe filter coefficients in the response to varying engine speed[13]. The transfer function of the second order Butterworthband-pass filter is given as:

(1)Note that the generic form of the transfer function has

been modified in recognition of the following relationships:b(2)=0, b(3)=−b(1), and a(1)=1.

Since filter coefficients b(1), a(2), and a(3) are enginespeed dependent, the final form of the knock signal extractionalgorithm may be represented using the model depicted inFig. 6, and the calculated filter coefficients in the full rangeof engine speeds are shown in Fig. 7. Examples of the knock

signal extracted with the CPDC/ISDC-embedded algorithmtogether with the results delivered by a laboratory-grade CASsystem are presented in Fig. 8.

Fig. 6. Simulink realization of knock extraction fromeither pressure or ionization current signal sampled at

constant distance in the crank angle domain.

Fig. 7. Dependence of 2nd order digital band-passButterworth filter coefficients on engine speed to matcha sampling system operating with a constant rate in the

crank angle domain.

Fig. 8. Engine knock level was raised in steps. The ionsense- based knock recording was scaled using amultiplier to convert the range of ion sense signal

comparable units.

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Combustion Phasing DetectionThe knock detection described above belongs to the

modelbased algorithm category since the data processinglogic is clearly linked to the well understood physics ofcombustion; in this case the modulation of the combustionchamber's gas density by an acoustic wave associated withknock. Other engine combustion parameters, like the locationof peak pressure or mass fraction burned, can also be inferredfrom the ion sense signal profile. However, so far, thedescription linking combustion kinetics with the gasionization, thus electric conductivity, is qualitative in natureonly (see [8, 10, 14,15,16,17]). Consequently, only a data-based approach can be adopted when building relevantcombustion algorithms based in ion sense signalinterpretation. With rather incomplete knowledge ofcorrelation between features of the ion sense signal andengine control combustion parameters, the application ofneural network techniques has been suggested and quiteintensely investigated (see for example [18,19,20,21,22,23]).

Fig. 9. An example of the Neural Network-based modelperformance of engine torque predicted with sparkadvance, rpm, intake manifold pressure, and EGR

fraction as neural network inputs. (A normal distributionis overlaid in red).

It became evident that a neural network trained with thekey input parameters (like engine speed, engine load, sparkadvance, etc.) would emulate a decent quality model/predictor of the engine output parameters. To illustrate thisfeature a simple five-node neural network was trained with

four input parameters: RPM, spark advance, MAP and EGR.The resultant algorithm delivers a good prediction of theengine torque as depicted in Fig. 9. Obviously, this is notgood enough to create any input for the engine combustioncontrol loop as the predictor does not provide informationspecific for individual combustion results. Any enginemalfunction like partial misfire would greatly affect theengine torque, but obviously is not considered by the neuralnetwork embedded model. The missing information which isutilized when calculating torques representing individualcombustion events is recovered by proper post-processing ofthe individual pressure traces by CAS. The same can be donewhen ion sense signal is used in place of pressure traces. Thisadditional input to a neural network provides informationunique to an individual combustion event and may prove tobe sufficient to convert the neural network-based predictorinto a virtual flame-evolution-based combustion sensor.

One of the challenges when designing a neural network isthe proper balance between the complexity of the neuralnetwork architecture and quality of the answers achieved atits output. Converting a neural network-based predictor intovirtual combustion sensor requires inclusion of the ion sensesignal in addition to of all the inputs needed forreconstruction of the predictor's formula in the neuralnetwork architecture. In most known applications of a neuralnetwork-based diagnostic, the number of the input neurons inthe hidden layer must be at least one node larger than thenumber of inputs. The neural network used in our example tocreate the engine torque predictor therefore has minimum of5 neurons. Thus, with four inputs the neural networkemulation code would perform 20 multiplications and 5additions in the hidden layer (plus five multiplications andone addition in the output layer if neural network consists ofone hidden layer). Adding an ion sense vector consisting ofsamples taken every 0.1 degree as was described whendiscussing knock detection would consume a prohibitiveamount of the digital data processing resources. Therefore,the ion sense signal vector must be reasonably decimated.Reasonably, because the vector size reduction techniqueneeds to preserve the information which will be required toefficiently aid the conversion of the predictor into a sensor.

A very efficient vector reduction technique is offered by awavelet filter analysis bank (see [22, 24]). However, thisdecimation technique becomes quite complex in thesituations where sampling does not occur in the time domain,like in the case of the ISDC. The other option successfullyused for ion sense vector size decimation [20, 22] is thePrincipal Component Analysis Method [25]. Briefly, as theoutcome of statistical analysis, the normalized ionizationsignal representation (statistical average) is created and theindividual signal's departure from average is measured atevery element of the digitized signal. This creates a basis forthe calculation of the correlation or covariance matrix whicheventually, when all variables are orthogonalized to avoidredundant information, provides a precise estimate of thecontribution of the reduced set of variables (initially equal in

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the number to number of input elements) to the total varianceof the original data. The whole process requires extensivecomputational resources, but once the matrix is calculatedand the decision on the number of principal components forreal time processing is made, the data reduction matrixalgorithm can be easily executed in real time. Thecontribution of the full set of principal components calculatedfrom over 300,000 recorded ionization current signals atvariety of engine operating conditions is shown in Fig. 10.The assessment of the result depicted in Fig. 10 is thetransformation of the input vector to a reduced vector that ismuch smaller, and contains only the critical information. Thisis symbolically shown in Fig. 11, where a 90% reduction inthe principal component vector size is deemed to deliverenough information about the features of ion sense signalcritical for the correct processing/interpretation of the data bya neural network.

Fig. 10. Full set of principal components (PCA matrixeigenvalues) of the ionization current signal indicating

the contribution of the variance of each individualprincipal component into the total variance of the

original data.

Fig. 11. Transformation and reduction of input vectorthru PCA to output vector.

Thus, a substantial reduction in the size of the neuralnetwork input vector, and thus number of hidden layer nodes,can be achieved without risk of significantly compromisingthe ion sense-based neural network algorithm performance.An example of the 50% of heat release/mass fraction burnedneural network-based algorithm performance is depicted in

the left column of Fig. 12 and location of peak pressureneural network-based algorithm in the right column (averageof 10 consecutive combustion events).

Fig. 12. Performance (error histograms and correlationplots) of neural network-based algorithms calculating

combustion phasing versus CAS reported results.

At certain engine running conditions, neural networkcalculations of the angular location of 50% of heat release aredisputable. To identify the source of the problem,dynamometer experiments were performed to provoke theerrant performance of the neural network-based CA50algorithm. The substandard performance was associated withsubstantial departure from the MBT operating point of theengine. For example, slewing the spark advance produced theCA50 readings as depicted in Fig. 13. There are, in general,two reasons for the algorithm to underperform. If, during thedata collection, the far-from-MBT points were rarelycollected, the neural network training would be performedwith insufficient representation of those operating conditions.This can be easily corrected by amending the data base withadditional experiments using those conditions.

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Fig. 13. Example of CA50 phasing as provided by thepressure-based CAS calculation (target) and ISDC-embedded ion sense-based algorithm together withcalculated absolute error between them. Encircled

portion highlights an area which requires additionalaction.

Fig. 14. CA50 calculation logic executed using dualneural networks. Combustion phasing output is refinedby interpolation of the individual ANN responses. RPM

was selected here as the neural network “switching”parameter.

Further improvement may require a bit more sophisticatedapproach. The range of variation of neural network inputparameters representing engine conditions (parametersdefining the specific predictor, i.e., not ionization currentrelated data) might be too wide to be handled by anindividual network of reasonable complexity. Then, the wideranging parameter needs to be singled out and the neuralnetwork trained with a fixed “troublesome” parameter. Sucha network will provide improved performance, but only atthis specific setting, say a single engine speed. However, thewhole engine speed range can be sub-divided into steps, sayevery 500 rpm, from engine idle to maximum speed. Asimple algorithm is used to interpolate results from twoneural network calculations and provide a diagnostic at anyspeed. This does of course increase the number of calibration

tables (weights and biases of individual neural networks), butonly doubles the real time computation load addressing theneeds of each individual neural network. Improvedperformance in the form of a “dual neural network” has beendemonstrated in the past when handling another application[26]. In short, the solution may look like the one,symbolically presented in Fig. 14.

Combustion Stability AlgorithmA combination of many different effects associated with

constantly changing cylinder conditions such as variation inignition and flame formation and its decay, etc., lead to somecombustion instability. Consequently, even if identical engineconditions are assumed, instantaneous torque maysignificantly depart from its theoretical prediction. Work wasdone to predict the torque fluctuations by monitoring thevariation in the pattern of the ionization current (see forexample [27]).

The CPDC/ISDC hardware features the crankshaft angle-based sampling block feeding an algorithm calculatinginstantaneous rpm. Therefore, instead of using still anotherdata-based interpretation technique, a model-driven IndicatedMean Effective Pressure (IMEP) technique based on thecrankshaft speed variation is utilized. The weakness of thismethod is that it delivers accurate estimates only at nearly-constant engine speed and fails to a certain extent during thetransients [28,29,30]. In the search for improving theperformance of this method, some modifications weresuggested by several authors [31,32,33,34] including amethod to amend the predictor with a reference IMEPseeding value [33, 34]. Since a very simple neural networkcan be assembled as an efficient torque predictor as shown inFig. 9, (or alternatively, the torque prediction can bedelivered by the ECM), the method adopted here is based onrpm fluctuations amended by a reference IMEP seeding. Asimulated response of the engine stability algorithmcalculating coefficient of variance of IMEP (COV of IMEP)in the case of a continuously underperforming cylinder of a 4-cylinder engine is depicted in Fig. 15. The first rowrepresents transient traces of IMEP of individual cylinders.The second row displays the calculated COV of IMEP ofindividual cylinders. Note that the underperforming cylinder'sCOV of IMEP hardly differs from the other cylinders sincethat cylinder steadily underperforms. The difference isevident only if all individual cylinders' IMEP arrays arecombined (row 3). Now, engine COV of IMEP becomesprohibitively high (row 4).

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Fig. 15. Underperformance of a single cylindersimulated by the CPDC/ISDC rpm-based engine stability

algorithm.

Obviously, the accuracy of the calculation of COV ofIMEP directly depends on the precision of the IMEP seedvalue. The ultimate assessment of the quality of thecombustion stability calculation is during engine torquetransients where rpm variance alone is unable to generateproperly scaled IMEP increment (increase or decrease) andrequires new seed values provided by the IMEP predictor.The key to success in handling transients is therefore themethod for updating the IMEP seed value. During transientoperation an intentional change in speed and/or load is beingcommanded. Consequently, the real-time running average ofCOV of IMEP is temporarily influenced by the transientevent.

Other Combustion Related AlgorithmsThe first mass production application of the ionization

current monitoring technique dates back to more than adecade ago. At that time it offered only misfire detection. Thephysics behind this application is intuitively obvious, sincelack of combustion leads to lack of detectable electricconductivity (provided by electric property of flame) andconsequently absence of the ion signal. The obviousextension of this approach would be to sense the presence ofthe ionization current when it is not desired, i.e. monitoringpre-ignition. For that task one needs, however, to open thesampling gate to cover a wider angular horizon and toanalyze the signal for additional features. Ignition coil ringinginterferes with the ionization current trace (see Fig. 16) butinformation is available before the start of the ringingactivity, and the ionization current phasing changes in favorof shifting of some key characteristic signal features to earlierpositions on the crank angle scale.

Fig. 16. Ionization current signal with exposed ignitioncoil ringing interference (signal flag was disabled).

Constant offset marked by encircled portion of the signalis one zone of the ion sense signal that can be analyzedto provide information on occurrence of pre-ignition.

ION SENSE DEVELOPMENTCONTROLLER

As mentioned in the introduction, a new ion-sense-basedcontroller (ISDC) for computing real-time combustionparameters has been developed. It operates in the crank-angledomain and has been demonstrated on a 4 cylinderapplication, but is capable of 8 cylinder analysis. Itincorporates a 32 bit floating point microprocessor, highspeed/high resolution (12 bit) A/D ion-current inputs, andutilizes advanced automotive grade circuitry, packaging,software methodologies, and architectural design whichenable autonomous and flexible integration into developmentvehicles. A picture of the ISDC is shown in Fig. 17 and ahigh level system diagram of the ISDC is shown in Fig. 18. Inaddition to the ion sense inputs, the ISDC also has directinputs from the crank, cam and MAP sensors andcommunicates to the vehicle ECM via a CAN bus. It alsosupports two other instrumentation CAN buses (plus anRS232 port) and has capabilities for logging data anddownloading the logged file to a laptop computer. Seereference [1] for further information on the characteristicsand capabilities of the CPDC, upon which the ISDC is based.

Coding Algorithms into ISDC ControllerThe ISDC controller development environment is based in

MATLAB/Simulink1. The algorithms are coded in bothSimulink and MATLAB embedded functions. A series ofalgorithms supporting internal data handling and datareduction, analog and digital I/O, combustion parametercomputations, output variable scaling, operation anddiagnostic flags, and CAN communication events areexecuted by the controller. Since the algorithms are requiredto be run for every combustion event, the ISDC executes the

1MATLAB and Simulink are trademarks or registered trademarks of The MathWorks, Inc.

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algorithms starting at bottom dead center (BDC) of theexpansion stroke. It is important that the ISDC is able tocomplete the necessary algorithms in real-time within a crankangle window (90 crank degrees in the case of an 8 cylinderapplication and 180 degrees in a 4 cylinder application) toensure it will not overlap with the computations for the nextcylinder event.

Fig. 17. Ion Sense Development Controller

Fig. 18. Ion Sense Development Controller High LevelSystem Diagram, 4-cylinder application.

One of the features offered by CPDC/ISDC embeddedsoftware is to monitor the execution time of the algorithm andreport remaining free time within an individual combustionevent. In addition to monitoring existing throughput status, itprovides guidance on surplus computational resourcesavailable for the future expansion of the software

functionality. Currently, even at maximum engine speeds, thealgorithm's execution time is far below the full capability ofthe microprocessor.

Communications with Engine ControllerIn order to effectively implement the above algorithms,

several operating parameters are required by the ISDC fromthe engine controller. These variables are transmitted overCAN to the ISDC to be used as inputs to the algorithms.

The ISDC also uses CAN to send the combustionparameters back to the engine controller. Directly followingthe power stroke, the ISDC sends one CAN message for eachcylinder event with Location of Peak Pressure, CA50, andknock feedback for that cylinder (along with other cylinder-specific combustion parameters). The ISDC also sends anadditional CAN message every engine cycle with COV ofIMEP information.

Closed-loop engine control is executed by the ECM andmodifies the relevant parameters on an individual-cylinder orglobal basis as appropriate.

SUMMARY/CONCLUSIONSA new real-time-capable, potentially mass production-

worthy combustion diagnostic technique has beendemonstrated. It is based on the ionization current signal anddigital signal processing. It employs a data-based approachwhere physical description of the underlying process is notsufficiently understood for the ionization signal. Wheneverexisting engineering knowledge is sufficient to create amodel, it utilizes model-based techniques commonlyemployed in the field. Algorithms demonstrated thus farprovide information on engine knock, combustion phasing (inthe form of location of peak pressure and mass fractionburned) and combustion stability. In addition, ongoing workis planned to incorporate algorithms for EGR fraction, fuelcomposition, complete or partial misfire, and detection ofpre-ignition. Full vehicle demonstration and verification isalso planned.

The advantage of ionization-current-based combustionfeedback is the immediate availability of the ionizationcurrent integrated ignition coils at a mass production cost.

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CONTACT INFORMATIONgerard.w.malaczynski@delphi.comgregory.roth@delphi.comdonald.v.johnson@delphi.com

ACKNOWLEDGMENTSThe authors wish to gratefully acknowledge the

contributions of the following people who supported thiswork:

Doug Sprunger, John Truong, Andre Scaff, Harry Husted,Karl Schten, Mike Conyers, Clint Erickson, Ash Punater,Chenghui Hao and Gene Ripley of Delphi AutomotiveSystems, LLC.

Ted Horvath, Kevin Freeman, Ken Hardman, MikePrucka, Ayman Ismail, Aakash Ahuja, Marv Corriea andSangeeta Theru of Chrysler Group LLC.

This material is based upon work supported by theDepartment of Energy under Award Number DE-EE0003347.

This report was prepared as an account of work sponsoredby an agency of the United States Government. Neither theUnited States Government nor any agency thereof, nor any oftheir employees, makes any warranty, express or implied, orassumes any legal liability or responsibility for the accuracy,completeness, or usefulness of any information, apparatus,product, or process disclosed, or represents that its use wouldnot infringe privately owned rights. Reference herein to anyspecific commercial product, process, or service by tradename, trademark, manufacturer, or otherwise does notnecessarily constitute or imply its endorsement,recommendation, or favoring by the United StatesGovernment or any agency thereof. The views and opinionsof authors expressed herein do not necessarily state or reflectthose of the United States Government or any agency thereof.

DEFINITIONS/ABBREVIATIONSA/D - analog-to-digitalASIC - application-specific integrated circuitBDC - bottom dead centerCA50 - crank angle 50CAN - Controller Area NetworkCAS - Combustion Analysis SystemCOV - coefficient of varianceCPDC - Cylinder Pressure Development ControllerECM - Engine Control ModuleEGR - exhaust gas recirculation

Malaczynski et al / SAE Int. J. Engines / Volume 6, Issue 1(May 2013)

I/O - input/outputIGBT - insulated gate bipolar transistorIMEP - indicated mean effective pressureISDC - Ion Sense Development ControllerMAP - manifold absolute pressureMBT - maximum brake torqueMFB - mass fraction burnedSA - spark advance

Malaczynski et al / SAE Int. J. Engines / Volume 6, Issue 1(May 2013)