The integration of sensors in engine components has been a long-standing wish of engine manufacturers and researchers. Conventional probes are particularly difficult to mount in moving engine parts and require time-intensive and costly preparation, while often still not reaching the site of interest close enough. The advances recently made in the field of printed electronics enable new possibilities for sensor integration that previously were not possible. Particularly, crankshaft engine bearings are an interesting component to apply those new sensors to. An important enabling factor for the successful sensor integration has been the increasing market penetration of polymer overlays for crankshaft bearings. The driving force behind this development was the pressure from legislation to reduce CO 2 emissions, which in turn brought about new technologies such as start-stop and mild hybrids. Engine components now have to operate in much more aggressive environments, which in many cases only polymer overlays withstand. The unique application process of those coatings together with their material properties, such as robustness and non-conductivity, now allow embedding of electronic components right at the running surface of the bearings. This paper details the development of a printed sensor that has been integrated into the bearing polymer overlay coating. Various results from respective rig testing of the sensor feedback throughout different load and speed conditions during operation are reported.

Cycle-to-cycle combustion variability (CV) in spark ignition internal combustion engines is amplified at high levels of exhaust gas recirculation (EGR) by sporadic partial burn and misfire events. A non-equiprobable cycle classification method, based on the magnitude of the indicated mean effective pressure (IMEP), was developed to discern and study the deterministic and stochastic components of cyclic CV. The time series analysis of experimental combustion cycles suggested that the occurrence of high energy release cycles right after misfires is the only deterministic component between consecutive cycles. This predictable behavior results from the retained air and fuel from the incomplete combustion cycle to the next. On the other hand, this study shows that the occurrence of partial burn and misfire cycles is the product of the stochastic component of cyclic CV with statistical properties similar to a multinomial probability distribution. It is demonstrated that observation of partial burns can increase the probability of observing a misfire when the conditional probability is used as the metric. Based on these findings, future work will be able to use the observation of partial burns alone to control the upper bound on the probability of misfire events. To this end, different metrics are proposed to control directly and indirectly the probability of misfires, and their advantages and disadvantages for feedback combustion control are discussed.

A novel decentralized control architecture is developed based on a feedback from the pressure difference across the engine which is responsible for the pumping losses and the Exhaust Gas Recirculation (EGR) flow in diesel engines. The controller is supplemented with another feedback loop based on NO x emissions measurement. Aiming for simple design and tuning, the two control loops are designed and discussed; one manipulates the Variable Geometry Turbine (VGT) actuator and the other manipulates the EGR valve. An experimentally validated mean-value diesel engine model is used to analyze the best pairing of actuators and set points. Emphasis is given to the robustness of this pairing based on gain changes across the entire operating region, since swapping the pairing needs to be avoided. The VGT loop is designed to achieve fast cylinder air charge increase in response to a rapid pedal tip-in by a feedforward term based on the real-time derivative of the desired boost pressure. The EGR loop relies on a feedback measurement from a NO x sensor and a real-time estimation of cylinder oxygen ratio, χ cyl . The engine model is used for evaluating the designed controllers over the federal test procedure (FTP) for heavy duty vehicles. Results indicate that the control system meets all targets, namely fast air charge and χ cyl control during torque transients, robust NO x control during steady state operation and controlled pumping losses in all conditions.

At a given speed and load, the spark advance (SA) is tuned to reach the maximum break torque (MBT) timing to maximize efficiency. The use of exhaust gas recirculation (EGR) can further improve fuel economy at the same speed and load. As EGR increases, MBT moves towards a more advanced timing that can be limited by the high variability in the combustion process, reflected in unacceptable torque fluctuations. This variability is rapidly increased by the random occurrence of partial burns and/or misfires. In order to operate close to the misfire limit, a stochastic misfire controller has been designed to momentarily move from an undesired to an allowable misfire rate, without significantly increasing variability in the combustion process. Control-oriented models for the combustion process and misfire events are discussed. Simulation of the closed-loop system shows that the feedback misfire controller, on average, stays closer to the misfire limit than a more conventional controller designed to react when a misfire is detected.

Motivated by increasingly strict NO x limits, engine manufactures have adopted selective catalytic reduction (SCR) technology to reduce engine-out NO x below mandated levels. In the SCR process, nitrogen oxides (NO x ) react with ammonia (NH 3 ) to form nitrogen and water vapor. The reaction is influenced by several variables, including stored ammonia on the catalyst, exhaust gas composition, and catalyst temperature. Currently, measurements from NO x and/or NH 3 sensors upstream and downstream of the SCR are used with predictive models to estimate ammonia storage levels on the catalyst and control urea dosing. This study investigated a radio frequency (RF) -based method to directly monitor the ammonia storage state of the SCR catalyst. This approach utilizes the SCR catalyst as a cavity resonator, in which an RF antenna excites electromagnetic waves within the cavity to monitor changes in the catalyst state. A mmonia storage causes changes in the dielectric properties of the catalyst, which directly impacts the RF signal. Changes in the RF signal relative to stored a mmonia (NH 3 ) were evaluated over a wide frequency range as well as temperature and exhaust conditions. The RF response to NH 3 storage, desorption, and oxidation on the SCR was observed to be well-correlated with changes in the catalyst state. Calibrated RF measurements demonstrate the ability to monitor the adsorption state of the SCR to within 10 % of the sensor full scale. The results indicate direct measurement of SCR ammonia storage levels, and resulting catalyst feedback control, via RF sensing to have significant potential for optimizing the SCR system to improve NO x conversion and decrease urea consumption.

The control of the combustion process is becoming a necessity for diesel engines in order to meet the upcoming stringent emission regulations. Ion current sensing technology has the potential to provide real-time feedback of the combustion process while using a fairly inexpensive sensor. 3D computational fluid dynamics (CFD) cycle simulation is becoming more complementary in understanding the complex combustion process in diesel engines. In this paper, a CFD study is focused on investigating the characteristics of the ion current signal produced during the combustion process of a Gen-set turbocharged diesel engine. Multiple virtual ion sensing probes are defined in different locations inside the combustion chamber to understand the influence of sensor location on signal characteristics. The n-heptane reaction mechanism and NO mechanism, combined with an ionization mechanism developed at WSU with 11 species, are used in the model to predict the chemical kinetics of combustion and the mole fraction of ionized species produced during combustion. Since the charge in diesel engines is heterogeneous and due to the sensing nature of the ion sensor, this paper explores the effect of sensor sensing diameter and its protrusion depth inside the combustion chamber on the ion current signal development. The simulation is validated by comparing in-cylinder pressure traces, the rate of heat release, and the ion current signal. Further, the model results are validated under different engine loads and injection pressures. This study utilizes the ionization mechanism to give further understanding of the complex formation of ionization species and their amplitudes, particularly at local sensing locations. This can be very vital to identify the potentials of using the ion current sensing and highlight its viability in feedback closed loop combustion control.

At the present time, both control and estimation accuracies of engine torque are causes for under-achieving optimal drivability and performance in today’s production vehicles. The major focus in this area has been to enhance torque estimation and control accuracies using existing open-loop torque control and estimation structures. Such an approach does not guarantee optimum torque tracking accuracy and optimum estimation accuracy due to air flow and efficiencies estimations errors. Furthermore, current approach overlooks the fast torque path tracking which does not have any related feedback. Recently, explicit torque feedback control has been proposed in the literature using either estimated or measured torques as feedback to control the torque using the slow torque path only. We propose the usage of a surface acoustic wave (SAW) torque sensor to measure the engine brake torque and feedback the signal to control the torque using both the fast and slow torque paths utilizing an inner-outer loop control structure. The fast torque path feedback is coordinated with the slow torque path by a novel method using the potential torque that is adapted to the sensor reading. The torque sensor signal enables a fast and explicit torque feedback control that can correct torque estimation errors and improve drivability, emission control, and fuel economy. Control-oriented engine models for the 3.6L engine are developed. Computer simulations are performed to investigate the advantages and limitations of the proposed control strategy, versus the existing strategies. The findings include an improvement of 14% in gain margin and 60% in phase margin when the torque feedback is applied to the cruise control torque request at the simulated operating point. This study demonstrates that the direct torque feedback is a powerful technology with promising results for improved powertrain performance and fuel economy.

High exhaust emissions reduction efficiencies from a spark ignited (SI) internal combustion engine utilizing a Non Selective Catalyst Reduction (NSCR) catalyst system require complex fuel control strategies. The allowable equivalence ratio (Φ) operating range is very narrow where NSCR systems achieve high exhaust emission reduction efficiencies of multiple species. Current fuel control technologies utilizing lambda sensor feedback for natural gas spark ignited engines are reported to be unable to sustain these demands for extended operation periods and when transients are introduced. Lambda sensor accuracy is the critical issue with current fuel controllers. The goal of this project was to develop a minimization control algorithm utilizing an oxides of nitrogen (NOx) sensor installed downstream of the NSCR catalyst system for feedback air/fuel ratio control. Testing was performed on a 100kW rated natural gas Cummins-Onan generator set that was reconfigured to operate utilizing an electronic gas carburetor (EGC2) with lambda sensor feedback and high reduction efficiency NSCR catalyst system. The control algorithm was programmed utilizing a Labview interface that communicated with the electronic gas carburetor where the fuel trim adjustment was physically made. Improvement under steady state operation was observed. The system was also evaluated during load and fuel composition transients.

With the increasingly stringent emissions and fuel economy standards, there is a need to develop new advanced in-cylinder sensing techniques to optimize the operation of internal combustion engine. In addition, reducing the number of on-board sensors needed for proper engine monitoring over the life time of the vehicle would reduce the cost and complexity of the electronic system. This paper presents a new technique to enable one engine component, the fuel injector, to perform multiple sensing tasks in addition to its primary task of delivering the fuel into the cylinder. The injector is instrumented within an electric circuit to produce a signal indicative of some injection and combustion parameters in electronically controlled spark ignition direct injection (SIDI) engines. The output of the multi sensing fuel injector (MSFI) system can be used as a feedback signal to the engine control unit (ECU) for injection timing control and diagnosis of the injection and combustion processes. A comparison between sensing capabilities of the multi-sensing fuel injector and the spark plug-ion sensor under different engine operating conditions is also included in this study. In addition, the combined use of the ion current signals produced by the MSFI and the spark plug for combustion sensing and control is demonstrated.

Ionization in internal combustion engines produces a signal indicative of in-cylinder conditions that can be used for the feedback electronic control of the engine, to meet production goals in performance, fuel economy and emissions. Most of the research has been conducted on carbureted and port injection spark ignition engines where the ionization mechanisms are well defined. A limited number of investigations have been conducted on ionization in diesel engines because of its complex combustion process. In this study, a detailed ionization mechanism is developed and introduced in a 3-D diesel cycle simulation computational fluid dynamics (CFD) code to determine the contribution of different species in the ionization process at different engine operating conditions. The CFD code is coupled with DARS-CFD, another module used to allow chemical kinetics calculations. The three-dimensional model accounts for the heterogeneity of the charge and the resulting variations in the combustion products. Furthermore, the model shows the effects of varying fuel injection pressure and engine load on the ion current signal characteristics. Ion current traces obtained experimentally from a heavy duty diesel engine were compared to the 3-D model results. The results of the simulation indicate that some heavy hydrocarbons, soot precursors play a major role, in addition to the role of NO x in ionization in diesel combustion.

Cold starting of diesel engines is characterized by inherent problems such as long cranking periods and combustion instability leading to an increase in fuel consumption and the emission of high concentrations of hydrocarbons which appear as white smoke. The ion current signal has been considered for the feedback control of both gasoline and diesel engines. However, the ion current signal produced from the combustion of the heterogeneous charge in diesel engines is weaker compared to that produced from the combustion of the homogeneous charge in gasoline engines. This presents a problem in the detection of the ion current signal in diesel engines, particularly during starting and idling operations. This paper investigates and addresses the ion current detection problems pertaining to cold starting and various idling speeds. Also, different approaches have been investigated to improve the signal detection under these conditions.

This paper describes an experimental study concerning the feasibility of monitoring the combustion instability levels of an HCCI engine based upon cycle-by-cycle exhaust temperature measurements. The test engine was a single cylinder, four-stroke, variable compression ratio Cooperative Fuel Research (CFR) engine coupled to an eddy current dynamometer. A rugged exhaust temperature sensor equipped with special signal processing circuitry was installed near the engine exhaust port. Reference measurements were provided by a laboratory grade, water-cooled cylinder pressure transducer. The cylinder pressure measurements were used to calculate the Coefficient of Variation of Indicated Mean Effective Pressure (COV of IMEP) for each operating condition tested. Experiments with the HCCI engine confirmed that cycle-by-cycle variations in exhaust temperature were present, and were of sufficient magnitude to be captured for processing as high fidelity signal waveforms. There was a good correlation between the variability of the exhaust temperature signal and the COV of IMEP throughout the operating range that was evaluated. The correlation was particularly strong at the low levels of COV of IMEP (2–3%), where production engines would typically operate. A real-time combustion instability signal was obtained from cycle-by-cycle exhaust temperature measurements, and used to provide feedback to the fuel injection control system. Closed loop operation of the HCCI engine was achieved in which the engine was operated as lean as possible while maintaining the COV level at or near 2.5%.

A limited amount of information exists on the effect of higher ethanol content fuel (greater than 10 vol%) for recreational vehicle engines. The possibility exists for misfueling of these vehicles, as ethanol content may increase at gas stations in the near future. Engine management systems in the recreational vehicle market are typically not equipped with feedback controls to adapt to the increased ethanol content. To address this concern and generate preliminary data related directly to the recreational industry, a study was conducted to evaluate the impact of E22 fuel on steady-state emissions and performance of two production snowmobiles. To fully analyze the impact of higher ethanol blends, cold-start, durability, and material compatibility tests should be performed, in conjunction with emissions and performance tests. While these additional tests were not performed as part of this study, there is a test program that is assessing all these factors on E15 fuel, which will be released in fall 2012. E0 fuel was used to establish baseline performance and emissions data. A 2009 four-stroke snowmobile with a 998cc, liquid-cooled, four-cylinder, intake port-fuel injected engine and a 2009 two-stroke snowmobile with a 599cc, liquid-cooled, two-cylinder, electronically controlled, crankcase-fuel injected engine were used for this study. Neither vehicle had any feedback air-fuel controls or after-treatment devices in the exhaust system. Power, fuel consumption, relevant engine temperatures, as well as, regulated exhaust emissions were recorded using the EPA 5-mode certification test cycle. The data showed no major impact on power output for either the four-stroke or two-stroke snowmobile. Brake specific fuel consumption varied with E22 as compared to E0. A reduction in CO emissions for both vehicles was observed for the E22 fuel. Both vehicles were factory calibrated rich of stoichiometric and hence, the addition of ethanol to the fuel effectively leaned out the air/fuel ratio and thus reduced the CO emissions. HC emissions were reduced for both the four-stroke and two-stroke engines, though certain test points of the two-stroke engine produced HC emissions that exceeded the analyzer measurement range (idle). Leaner operation reduced HC formation. Exhaust gas temperatures were observed to increase from 20°C – 50°C with E22 fuel, due to enleanment.

Accurate combustion analysis at the test bed is an important tool for the development engineer. It can help engine design, efficiency improvements or emissions reduction by providing instantaneous feedback on the combustion process. It can also provide detailed combustion information to help speed-up the engine calibration process. By implementing shared memory communication, multiple core capability and streamlined calculation techniques, the calculation time of AVL gas exchange and combustion analysis software GCA (Gas Exchange and Combustion Analysis) has been dramatically reduced without significantly decreasing calculation results accuracy. This allows AVL IndiCOM (in combination with AVL GCA) to perform accurate gas exchange and combustion analysis calculations directly and promptly at the test bed. This opens the door to a number of promising new applications by erasing the bridge between measurement data acquisition and post-processing analysis. Increase of measurement data consistency and the reduction of development time are two of the most important benefits of being able to perform “on-line” plausibility checks of measurement data. The strong links connecting AVL GCA calculation results to the measurement data and the redundancy between calculation and measurement for the assessment of some highly relevant engine parameters (e.g. IMEP, air mass flow) can greatly extend the “on-line” plausibility checks functions already available in AVL IndiCom or AVL test bed automation software PUMA. Some passenger car application examples, where valve train flexibility is used to enhance fuel economy or reduce exhaust emissions of internal combustion engines, show that the immediate availability at the test bed of gas exchange related parameters (e.g. internal EGR rate, scavenged mass, mass flows through the valves) supports an intuitive optimization of the valve train parameters.

Combustion control is a crucial aspect in modern Diesel engines control strategies, mainly due to the requests to increase efficiency and maintain pollutant emissions within the values bounded by standard regulations. In order to perform an accurate combustion control, modern “closed loop” control algorithms require the evaluation of a large number of quantities that provide information about combustion process effectiveness. This work presents a methodology that allows real-time estimation of energy released, during the combustion process, in a Common Rail Multi-Jet Diesel engine. This procedure can be divided in two main steps. The first step consists in the development of a zero-dimensional combustion model based on the linear combination of a proper number of Wiebe functions. In this case, a zero-dimensional approach has been chosen, because it is accurate enough for this analysis and requires low computational efforts. Once the combustion model has been developed, it can be used to determine Rate of Heat Release (RoHR) and the angular position in which 50% of fuel burned within an engine cycle is reached (MFB50). The second section of this work describes the relationships existing between injection parameters (such as Start of Injection, injected fuel quantities, rail pressure...) and the Wiebe parameters identified in the first step of the procedure. The above mentioned relationships have been used to set up correlations that allow estimating Wiebe parameters, therefore ROHR and MFB50, starting from injection parameters. The results obtained in MFB50 estimation are particularly emphasized, because real-time knowledge of this quantity is necessary to feedback a control algorithm for optimal combustion positioning. This work is based on several experimental tests performed on a 2.2 liters Common Rail Multi-Jet Diesel engine. First, experimental tests have been carried out to identify the combustion model and the correlations existing between Wiebe parameters and injection parameters. Then, in order to determine the accuracy of the approach, the complete estimation methodology has been applied to the engine under study. This work describes a methodology for real-time estimation of several quantities that provide important information about combustion process effectiveness (useful, for example, in modern low temperature combustion control systems). No extra cost is needed, because the methodology requires no additional sensor.

The increasingly strict emission regulations may require implementing Non-Selective Catalytic Reduction (NSCR) system as a promising emission control technology for stationary rich burn spark ignition engines. Many recent investigations used NSCR systems for stationary natural gas fueled engines showed that NSCR systems were unable to consistently control the emissions level below the compliance limits. Modeling of NSCR components to better understand, and then exploit, the underlying physical processes that occur in the lambda sensor and the catalyst media is now considered an essential step toward the required NSCR system performance. This paper presents the work done to date on a modeling of lambda sensor that provides feedback to the air-to-fuel controller. Several recent experimental studies indicate that the voltage signal from the lambda sensor may not be interpreted correctly because of the physical nature in the way the sensor senses the exhaust gas concentration. Correct interpretation of the sensor output signal is necessary to achieve consistently low emissions level. The goal of this modeling study is to improve the understanding of the physical processes that occur within the sensor, investigate the cross-sensitivity of various exhaust gas species on the sensor performance, and finally this model serves as a tool to improve NSCR control strategies. This model simulates the output from a planar switch type lambda sensor. The model consists of three modules. The first module models the multi-component mass transport through the sensor protective layer. Diffusion fluxes are calculated using the Maxwell-Stefan equation. The second module includes all the surface catalytic reactions that take place on the sensor platinum electrodes. All kinetic reactions are modeled based on the Langmuir-Hinshelwood kinetic mechanism. The model incorporates for the first time methane catalytic reactions on the sensor platinum electrode. The third module is responsible for simulating the reactions that occur on the electrolyte material and determine the sensor output voltage. The model results are validated using field test data obtained from a mapping study of a natural gas-fueled engine equipped with NSCR system. The data showed that the lambda sensor output voltage is influenced by the reducing species concentration, such as carbon monoxide (CO) and hydrogen (H 2 ). The results from the developed model and the experimental data showed strong correlations between CO and H 2 with the sensor output voltage within the lambda operating range between 0.994 to 1.007 (catalytic converter operating window). This model also showed that methane does not significantly influence the lambda sensor performance compared to the effect of CO and H 2 .

The novel application of the Discrete Wavelet Transform (DWT) in a real time controller is used to detect and subsequently control knock in a Homogeneous Charge Compression Ignition (HCCI) engine. Classical Fourier techniques for knock detection are discussed and compared to Wavelet Transforms. The Discrete Wavelet Transform filter bank is chosen as the best method for knock detection due to its good time-resolution and low computational requirements. The DWT method is compared with the root mean squared value of the pressure trace as the benchmark method for determining knock and the two methods are linearly correlated. Using the DWT method for knock detection and modulating fuel octane, both a Proportional Integral (PI) and PI with Feed-forward control are implemented. Both of these methods reduce knock intensity for a step increase in engine load. The combination of Feed-forward with PI feedback is found to be slightly more effective than just PI feedback control.

Automakers and the car-buying public maintain a strong and continuing interest in enhanced vehicle efficiency. Ideally, adaptively controlled oil pumps supply only enough flow within an engine to satisfy its performance requirements. Any extra flow wastes energy. In order to better understand how to improve engine and engine oil circuit efficiency, and to assess pump stability, a detailed dynamic model of a variable displacement vane pump (VDVP) is developed. This detailed pump model is mated to a simplified engine oil circuit model. This marriage allows for a detailed prediction of pump response under various simulated engine operating conditions. The VDVP modeled here adapts its pump chamber volumes according to 1) the feedback oil pressure provided from the engine oil circuit and 2) according to the sizing and installed compression loading of an internal spring. Many phenomena such as internal leakage from one pump chamber volume to another, variable oil conditions such as aeration and viscosity, as well as variations in choice for the internal spring rate and preload can be investigated for their effects on oil pump behavior and performance within the simplified oil circuit.

This paper discusses the use of an ion current sensor to detect combustion resonance in a heavy duty direct injection diesel engine. A modified glow plug is used to measure the ion current in addition to its main function in warming up the combustion chamber. A comparison is made between the combustion resonance determined from the signals of an ion current sensor, a cylinder pressure transducer and an engine vibration sensor. Experiments are conducted on a four cylinder, turbo-charged 4.5L diesel engine to determine the potential of using the ion current sensor to detect combustion resonance under different injection pressures and exhaust gas recirculation rates. It is concluded that the ion current signal can be used to determine the timing, amplitude, frequency and duration of the resonance. The sensor output has the potential to be used as a feedback signal to the ECU (Electronic Control Unit) to minimize engine vibration and noise.

Large industrial internal combustion engines employ a wide range of analytical techniques in order to improve their availability and fuel efficiency, and to decrease their maintenance expense. When viewed in context of reliability centered maintenance programs, most industrial engines rely on one of the following systems: • Time-based preventive maintenance analyses, • Continuous monitoring systems that have first-principles deterministic models, or • Run until failure operating philosophy. Predictive analytics using empirically-derived pattern recognition algorithms can enhance problem detection and increase the effectiveness of an organization’s monitoring program. With robust early detection that has few false alarms, equipment operators can remove time-dependent randomness from their ability to discover problems. Skilled equipment analysts and technicians will no longer spend time devoted to analyzing healthy equipment. When a company has numerous engine installations, scalability and cost-effectiveness becomes increasingly more important. Empirically-derived pattern recognition speeds fleet deployment across a wide range of equipment types and models. This reduces the dependence on the expertise that is required to establish and maintain a first-principles based system. Predictive analytics enables a bridge between the depth of coverage of a permanently installed full analytical system and the versatility of portable analyzers and preventive maintenance analyses. Equipment operators can use early detection from predictive analytics to focus their technicians on analyzing the right equipment at the right time. This paper will describe the predictive analytics modeling philosophy around frame mechanics, combustion, and emissions. One or more case studies will show the processes of even detection, diagnostics, collaboration, and information consolidation. The extension of predictive analytics is predictive diagnostics which combines detection with the context of how equipment operates. Equipment operators can extend run times and maintenance intervals by using predictive analytics as a foundation. The observations, diagnoses, and feedback will then roll up into a total asset management system and bridge major gaps that occur in many reliability programs. Predictive diagnostic methodologies enable equipment owners to extend run times and to decrease maintenance effort with confidence.