The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
Claudio Forte - One of the best experts on this subject based on the ideXlab platform.
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Transient Spark Advance Calibration Approach
Energy Procedia, 2014Co-Authors: Enrico Corti, Nicolò Cavina, Alberto Cerofolini, Claudio Forte, Giorgio Mancini, Davide Moro, Fabrizio Ponti, Vittorio RavaglioliAbstract:Abstract Combustion control is assuming a crucial role in reducing engine tailpipe emissions while maximizing performance. The effort in the calibration of control parameters affecting the combustion development can be very demanding. One of the most effective factors influencing performance and efficiency is the combustion phasing: in Spark Ignition (SI) engines it is affected by factors such as Spark Advance (SA), Air-Fuel Ratio (AFR), Exhaust Gas Recirculation (EGR), Variable Valve Timing (VVT). SA optimal values are usually determined by means of calibration procedures carried out in steady state conditions on the test bench by changing SA values while monitoring performance indicators, such as Brake and Indicated Mean Effective Pressure (BMEP, IMEP), Brake Specific Fuel Consumption (BSFC) and pollutant emissions. The effect of SA on combustion is stochastic, due to the cycle-to-cycle variation: the analysis of mean values requires many engine cycles to be significant of the performance obtained with the given control setting. Moreover, often the effect of SA on engine performance must be investigated for different settings of other control parameters (EGR, VVT, AFR). The calibration process is time consuming involving exhaustive tests followed by off-line data analysis. This paper presents the application of a dynamic calibration methodology, with the objective of reducing the calibration duration. The proposed approach is based on transient tests, coupled with a statistical investigation, allowing reliable performance analysis even with a low number of engine cycles. The methodology has been developed and tested off-line, then it has been implemented in Real-Time. The combustion analysis system has been integrated with the ECU management software and the test bench controller, in order to perform a fully automatic calibration.
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Spark Advance Real-Time Optimization Based on Combustion Analysis
Journal of Engineering for Gas Turbines and Power, 2011Co-Authors: Enrico Corti, Claudio ForteAbstract:Future emission regulations could force manufacturers to install in-cylinder pressure sensors on production engines. The availability of such a signal opens a new scenario in terms of combustion control: many settings that previously were optimized off-line, can now be monitored and calibrated in realtime. One of the most effective factors influencing performance and efficiency is the combustion phasing: in gasoline engines Electronic Control Units (ECU) manage the Spark Advance (SA) in order to set the optimal combustion phase. SA optimal values are usually determined by means of calibration procedures carried out on the test bench by changing the ignition angle while monitoring Brake and Indicated Mean Effective Pressure (BMEP, IMEP) and Brake Specific Fuel Consumption (BSFC). The optimization process relates BMEP, IMEP and BSFC mean values with the control setting (SA). However, the effect of SA on combustion is not deterministic, due to the cycle-to-cycle variation: the analysis of mean values requires many engine cycles to be significant of the performance obtained with the given control setting. This paper presents a novel approach to SA optimization, with the objective of improving the performance analysis robustness, while reducing the test time. The approach can be either used in the calibration phase or in on-board applications, if the in-cylinder pressure signal is available: this would allow maintaining the optimization active throughout the entire engine life. The methodology is based on the observation that, for a given running condition, IMEP can be considered a function of a single combustion parameter, represented by the 50% Mass Fraction Burned (50%MFB). Due to cycle-to-cycle variation, many different MFB50 and IMEP values are obtained during a steady state test carried out with constant SA, but these values are related by means of a unique relationship. The distribution on the plane IMEP-MFB50 forms a parabola, therefore the optimization could be carried out by choosing SA values maintaining the scatter around the vertex. Unfortunately the distribution shape is slightly influenced by heat losses (i.e., by SA): this effect must be taken into account in order to avoid over-Advanced calibrations. SA is then controlled by means of a PID (Proportional Integer Derivative) controller, fed by an error that is defined based on the previous considerations: a contribution is related to the MFB50-IMEP distribution, and a second contribution is related to the net Cumulative Heat Release (CHRNET )-IMEP distribution. The latter is able to take into account for heat losses. Firstly, the methodology has been tested on in-cylinder pressure data, collected from different SI engines; then, it has been implemented in real-time, by means of a programmable combustion analyzer: the system performs a cycle-to-cycle combustion analysis, evaluating the combustion parameters necessary to calculate the target SA, which is then actuated by the ECU. The approach proved to be efficient, reducing the number of engine cycles necessary for the calibration to less than 1000 per operating condition.Copyright © 2010 by ASME
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A Statistical Approach to Spark Advance Mapping
Journal of Engineering for Gas Turbines and Power, 2010Co-Authors: Enrico Corti, Claudio ForteAbstract:Engine performance and efficiency are largely influenced by combustion phasing. Operating conditions and control settings influence the combustion development over the crankshaft angle; the most effective control parameter used by electronic control units to optimize the combustion process for Spark ignition engines is Spark Advance (SA). SA mapping is a time-consuming process, usually carried out with the engine running in steady state on the test bench, changing SA values while monitoring brake mean effective pressure, indicated mean effective pressure (IMEP), and brake specific fuel consumption (BSFC). Mean values of IMEP and BSFC for a test carried out with a given SA setting are considered as the parameters to optimize. However, the effect of SA on IMEP and BSFC is not deterministic, due to the cycle-to-cycle variation; the analysis of mean values requires many engine cycles to be significant of the performance obtained with the given control setting. Finally, other elements such as engine or components aging, and disturbances like air-to-fuel ratio or air, water, and oil temperature variations could affect the tests results; this facet can be very significant for racing engine testing. This paper presents a novel approach to SA mapping with the objective of improving the performance analysis robustness while reducing the test time. The methodology is based on the observation that, for a given running condition, IMEP can be considered a function of the combustion phasing, represented by the 50% mass fraction burned (MFB50) parameter. Due to cycle-to-cycle variation, many different MFB50 and IMEP values are obtained during a steady state test carried out with constant SA. While MFB50 and IMEP absolute values are influenced by disturbance factors, the relationship between them holds, and it can be synthesized by means of the angular coefficient of the tangent line to the MFB50-IMEP distribution. The angular coefficient variations as a function of SA can be used to feed a SA controller, able to maintain the optimal combustion phasing. Similarly, knock detection is approached by evaluating two indexes; the distribution of a typical knock-sensitive parameter (maximum amplitude of pressure oscillations) is related to that of CHRNET (net cumulative heat release), determining a robust knock index. A knock limiter controller can then be added in order to restrict the SA range to safe values. The methodology can be implemented in real time combustion controllers; the algorithms have been applied offline to sampled data, showing the feasibility of fast and robust automatic mapping procedures.
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Spark Advance Real-Time Optimization Based on Combustion Analysis
ASME 2010 Internal Combustion Engine Division Fall Technical Conference, 2010Co-Authors: Enrico Corti, Claudio ForteAbstract:Future emission regulations could force manufacturers to install in-cylinder pressure sensors on production engines. The availability of such a signal opens a new scenario in terms of combustion control: many settings that previously were optimized off-line, can now be monitored and calibrated in realtime. One of the most effective factors influencing performance and efficiency is the combustion phasing: in gasoline engines Electronic Control Units (ECU) manage the Spark Advance (SA) in order to set the optimal combustion phase. SA optimal values are usually determined by means of calibration procedures carried out on the test bench by changing the ignition angle while monitoring Brake and Indicated Mean Effective Pressure (BMEP, IMEP) and Brake Specific Fuel Consumption (BSFC). The optimization process relates BMEP, IMEP and BSFC mean values with the control setting (SA). However, the effect of SA on combustion is not deterministic, due to the cycle-to-cycle variation: the analysis of mean values requires many engine cycles to be significant of the performance obtained with the given control setting. This paper presents a novel approach to SA optimization, with the objective of improving the performance analysis robustness, while reducing the test time. The approach can be either used in the calibration phase or in on-board applications, if the in-cylinder pressure signal is available: this would allow maintaining the optimization active throughout the entire engine life. The methodology is based on the observation that, for a given running condition, IMEP can be considered a function of a single combustion parameter, represented by the 50% Mass Fraction Burned (50%MFB). Due to cycle-to-cycle variation, many different MFB50 and IMEP values are obtained during a steady state test carried out with constant SA, but these values are related by means of a unique relationship. The distribution on the plane IMEP-MFB50 forms a parabola, therefore the optimization could be carried out by choosing SA values maintaining the scatter around the vertex. Unfortunately the distribution shape is slightly influenced by heat losses (i.e., by SA): this effect must be taken into account in order to avoid over-Advanced calibrations. SA is then controlled by means of a PID (Proportional Integer Derivative) controller, fed by an error that is defined based on the previous considerations: a contribution is related to the MFB50-IMEP distribution, and a second contribution is related to the net Cumulative Heat Release (CHRNET)-IMEP distribution. The latter is able to take into account for heat losses. Firstly, the methodology has been tested on in-cylinder pressure data, collected from different SI engines; then, it has been implemented in real-time, by means of a programmable combustion analyzer: the system performs a cycle-to-cycle combustion analysis, evaluating the combustion parameters necessary to calculate the target SA, which is then actuated by the ECU. The approach proved to be efficient, reducing the number of engine cycles necessary for the calibration to less than 1000 per operating condition.
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A Statistical Approach to Spark Advance Mapping
ASME 2009 Internal Combustion Engine Division Spring Technical Conference, 2009Co-Authors: Enrico Corti, Claudio ForteAbstract:Engines performance and efficiency are largely influenced by the combustion phasing. Operating conditions and control settings influence the combustion development over the crankshaft angle: the most effective control parameter used by Electronic Control Units (ECU) to optimize the combustion process for Spark Ignition (SI) engines is Spark Advance (SA). SA mapping is a time-consuming process, usually carried out with the engine running in steady state on the test bench, changing SA values while monitoring Brake and Indicated Mean Effective Pressure (BMEP, IMEP) and Brake Specific Fuel Consumption (BSFC). Mean values of IMEP and BSFC for a test carried out with a given SA setting are considered as the parameters to optimize. However, the effect of SA on IMEP and BSFC is not deterministic, due to the cycle-to-cycle variation: the analysis of mean values requires many engine cycles to be significant of the performance obtained with the given control setting. Finally other elements, such as engine or components ageing, and disturbances like Air-to-Fuel Ratio (AFR) or air, water and oil temperature variations, could affect the tests results: this facet can be very significant for racing engines testing. This paper presents a novel approach to SA mapping, with the objective of improving the performance analysis robustness, while reducing the test time. The methodology is based on the observation that, for a given running condition, IMEP can be considered a function of the combustion phasing, represented by the 50% Mass Fraction Burned (MFB50) parameter. Due to cycle-to-cycle variation, many different MFB50 and IMEP values are obtained during a steady state test carried out with constant SA. While MFB50 and IMEP absolute values are influenced by disturbance factors, the relationship between them holds, and it can be synthesized by means of the angular coefficient of the tangent line to the MFB50-IMEP distribution. The angular coefficient variations as a function of SA can be used to feed a SA controller, able to maintain the optimal combustion phasing. Similarly, knock detection is approached by evaluating two indexes: the distribution of a typical knock-sensitive parameter (MAPO, Maximum Amplitude of Pressure Oscillations) is related to that of CHRNET (net Cumulative Heat Release), determining a robust knock index. A knock limiter controller can then be added, in order to restrict the SA range to safe values. The methodology can be implemented in real-time combustion controllers: the algorithms have been applied offline to sampled data, showing the feasibility of fast and robust automatic mapping procedures.Copyright © 2009 by ASME
Enrico Corti - One of the best experts on this subject based on the ideXlab platform.
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Transient Spark Advance Calibration Approach
Energy Procedia, 2014Co-Authors: Enrico Corti, Nicolò Cavina, Alberto Cerofolini, Claudio Forte, Giorgio Mancini, Davide Moro, Fabrizio Ponti, Vittorio RavaglioliAbstract:Abstract Combustion control is assuming a crucial role in reducing engine tailpipe emissions while maximizing performance. The effort in the calibration of control parameters affecting the combustion development can be very demanding. One of the most effective factors influencing performance and efficiency is the combustion phasing: in Spark Ignition (SI) engines it is affected by factors such as Spark Advance (SA), Air-Fuel Ratio (AFR), Exhaust Gas Recirculation (EGR), Variable Valve Timing (VVT). SA optimal values are usually determined by means of calibration procedures carried out in steady state conditions on the test bench by changing SA values while monitoring performance indicators, such as Brake and Indicated Mean Effective Pressure (BMEP, IMEP), Brake Specific Fuel Consumption (BSFC) and pollutant emissions. The effect of SA on combustion is stochastic, due to the cycle-to-cycle variation: the analysis of mean values requires many engine cycles to be significant of the performance obtained with the given control setting. Moreover, often the effect of SA on engine performance must be investigated for different settings of other control parameters (EGR, VVT, AFR). The calibration process is time consuming involving exhaustive tests followed by off-line data analysis. This paper presents the application of a dynamic calibration methodology, with the objective of reducing the calibration duration. The proposed approach is based on transient tests, coupled with a statistical investigation, allowing reliable performance analysis even with a low number of engine cycles. The methodology has been developed and tested off-line, then it has been implemented in Real-Time. The combustion analysis system has been integrated with the ECU management software and the test bench controller, in order to perform a fully automatic calibration.
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Spark Advance Real-Time Optimization Based on Combustion Analysis
Journal of Engineering for Gas Turbines and Power, 2011Co-Authors: Enrico Corti, Claudio ForteAbstract:Future emission regulations could force manufacturers to install in-cylinder pressure sensors on production engines. The availability of such a signal opens a new scenario in terms of combustion control: many settings that previously were optimized off-line, can now be monitored and calibrated in realtime. One of the most effective factors influencing performance and efficiency is the combustion phasing: in gasoline engines Electronic Control Units (ECU) manage the Spark Advance (SA) in order to set the optimal combustion phase. SA optimal values are usually determined by means of calibration procedures carried out on the test bench by changing the ignition angle while monitoring Brake and Indicated Mean Effective Pressure (BMEP, IMEP) and Brake Specific Fuel Consumption (BSFC). The optimization process relates BMEP, IMEP and BSFC mean values with the control setting (SA). However, the effect of SA on combustion is not deterministic, due to the cycle-to-cycle variation: the analysis of mean values requires many engine cycles to be significant of the performance obtained with the given control setting. This paper presents a novel approach to SA optimization, with the objective of improving the performance analysis robustness, while reducing the test time. The approach can be either used in the calibration phase or in on-board applications, if the in-cylinder pressure signal is available: this would allow maintaining the optimization active throughout the entire engine life. The methodology is based on the observation that, for a given running condition, IMEP can be considered a function of a single combustion parameter, represented by the 50% Mass Fraction Burned (50%MFB). Due to cycle-to-cycle variation, many different MFB50 and IMEP values are obtained during a steady state test carried out with constant SA, but these values are related by means of a unique relationship. The distribution on the plane IMEP-MFB50 forms a parabola, therefore the optimization could be carried out by choosing SA values maintaining the scatter around the vertex. Unfortunately the distribution shape is slightly influenced by heat losses (i.e., by SA): this effect must be taken into account in order to avoid over-Advanced calibrations. SA is then controlled by means of a PID (Proportional Integer Derivative) controller, fed by an error that is defined based on the previous considerations: a contribution is related to the MFB50-IMEP distribution, and a second contribution is related to the net Cumulative Heat Release (CHRNET )-IMEP distribution. The latter is able to take into account for heat losses. Firstly, the methodology has been tested on in-cylinder pressure data, collected from different SI engines; then, it has been implemented in real-time, by means of a programmable combustion analyzer: the system performs a cycle-to-cycle combustion analysis, evaluating the combustion parameters necessary to calculate the target SA, which is then actuated by the ECU. The approach proved to be efficient, reducing the number of engine cycles necessary for the calibration to less than 1000 per operating condition.Copyright © 2010 by ASME
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A Statistical Approach to Spark Advance Mapping
Journal of Engineering for Gas Turbines and Power, 2010Co-Authors: Enrico Corti, Claudio ForteAbstract:Engine performance and efficiency are largely influenced by combustion phasing. Operating conditions and control settings influence the combustion development over the crankshaft angle; the most effective control parameter used by electronic control units to optimize the combustion process for Spark ignition engines is Spark Advance (SA). SA mapping is a time-consuming process, usually carried out with the engine running in steady state on the test bench, changing SA values while monitoring brake mean effective pressure, indicated mean effective pressure (IMEP), and brake specific fuel consumption (BSFC). Mean values of IMEP and BSFC for a test carried out with a given SA setting are considered as the parameters to optimize. However, the effect of SA on IMEP and BSFC is not deterministic, due to the cycle-to-cycle variation; the analysis of mean values requires many engine cycles to be significant of the performance obtained with the given control setting. Finally, other elements such as engine or components aging, and disturbances like air-to-fuel ratio or air, water, and oil temperature variations could affect the tests results; this facet can be very significant for racing engine testing. This paper presents a novel approach to SA mapping with the objective of improving the performance analysis robustness while reducing the test time. The methodology is based on the observation that, for a given running condition, IMEP can be considered a function of the combustion phasing, represented by the 50% mass fraction burned (MFB50) parameter. Due to cycle-to-cycle variation, many different MFB50 and IMEP values are obtained during a steady state test carried out with constant SA. While MFB50 and IMEP absolute values are influenced by disturbance factors, the relationship between them holds, and it can be synthesized by means of the angular coefficient of the tangent line to the MFB50-IMEP distribution. The angular coefficient variations as a function of SA can be used to feed a SA controller, able to maintain the optimal combustion phasing. Similarly, knock detection is approached by evaluating two indexes; the distribution of a typical knock-sensitive parameter (maximum amplitude of pressure oscillations) is related to that of CHRNET (net cumulative heat release), determining a robust knock index. A knock limiter controller can then be added in order to restrict the SA range to safe values. The methodology can be implemented in real time combustion controllers; the algorithms have been applied offline to sampled data, showing the feasibility of fast and robust automatic mapping procedures.
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Spark Advance Real-Time Optimization Based on Combustion Analysis
ASME 2010 Internal Combustion Engine Division Fall Technical Conference, 2010Co-Authors: Enrico Corti, Claudio ForteAbstract:Future emission regulations could force manufacturers to install in-cylinder pressure sensors on production engines. The availability of such a signal opens a new scenario in terms of combustion control: many settings that previously were optimized off-line, can now be monitored and calibrated in realtime. One of the most effective factors influencing performance and efficiency is the combustion phasing: in gasoline engines Electronic Control Units (ECU) manage the Spark Advance (SA) in order to set the optimal combustion phase. SA optimal values are usually determined by means of calibration procedures carried out on the test bench by changing the ignition angle while monitoring Brake and Indicated Mean Effective Pressure (BMEP, IMEP) and Brake Specific Fuel Consumption (BSFC). The optimization process relates BMEP, IMEP and BSFC mean values with the control setting (SA). However, the effect of SA on combustion is not deterministic, due to the cycle-to-cycle variation: the analysis of mean values requires many engine cycles to be significant of the performance obtained with the given control setting. This paper presents a novel approach to SA optimization, with the objective of improving the performance analysis robustness, while reducing the test time. The approach can be either used in the calibration phase or in on-board applications, if the in-cylinder pressure signal is available: this would allow maintaining the optimization active throughout the entire engine life. The methodology is based on the observation that, for a given running condition, IMEP can be considered a function of a single combustion parameter, represented by the 50% Mass Fraction Burned (50%MFB). Due to cycle-to-cycle variation, many different MFB50 and IMEP values are obtained during a steady state test carried out with constant SA, but these values are related by means of a unique relationship. The distribution on the plane IMEP-MFB50 forms a parabola, therefore the optimization could be carried out by choosing SA values maintaining the scatter around the vertex. Unfortunately the distribution shape is slightly influenced by heat losses (i.e., by SA): this effect must be taken into account in order to avoid over-Advanced calibrations. SA is then controlled by means of a PID (Proportional Integer Derivative) controller, fed by an error that is defined based on the previous considerations: a contribution is related to the MFB50-IMEP distribution, and a second contribution is related to the net Cumulative Heat Release (CHRNET)-IMEP distribution. The latter is able to take into account for heat losses. Firstly, the methodology has been tested on in-cylinder pressure data, collected from different SI engines; then, it has been implemented in real-time, by means of a programmable combustion analyzer: the system performs a cycle-to-cycle combustion analysis, evaluating the combustion parameters necessary to calculate the target SA, which is then actuated by the ECU. The approach proved to be efficient, reducing the number of engine cycles necessary for the calibration to less than 1000 per operating condition.
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A Statistical Approach to Spark Advance Mapping
ASME 2009 Internal Combustion Engine Division Spring Technical Conference, 2009Co-Authors: Enrico Corti, Claudio ForteAbstract:Engines performance and efficiency are largely influenced by the combustion phasing. Operating conditions and control settings influence the combustion development over the crankshaft angle: the most effective control parameter used by Electronic Control Units (ECU) to optimize the combustion process for Spark Ignition (SI) engines is Spark Advance (SA). SA mapping is a time-consuming process, usually carried out with the engine running in steady state on the test bench, changing SA values while monitoring Brake and Indicated Mean Effective Pressure (BMEP, IMEP) and Brake Specific Fuel Consumption (BSFC). Mean values of IMEP and BSFC for a test carried out with a given SA setting are considered as the parameters to optimize. However, the effect of SA on IMEP and BSFC is not deterministic, due to the cycle-to-cycle variation: the analysis of mean values requires many engine cycles to be significant of the performance obtained with the given control setting. Finally other elements, such as engine or components ageing, and disturbances like Air-to-Fuel Ratio (AFR) or air, water and oil temperature variations, could affect the tests results: this facet can be very significant for racing engines testing. This paper presents a novel approach to SA mapping, with the objective of improving the performance analysis robustness, while reducing the test time. The methodology is based on the observation that, for a given running condition, IMEP can be considered a function of the combustion phasing, represented by the 50% Mass Fraction Burned (MFB50) parameter. Due to cycle-to-cycle variation, many different MFB50 and IMEP values are obtained during a steady state test carried out with constant SA. While MFB50 and IMEP absolute values are influenced by disturbance factors, the relationship between them holds, and it can be synthesized by means of the angular coefficient of the tangent line to the MFB50-IMEP distribution. The angular coefficient variations as a function of SA can be used to feed a SA controller, able to maintain the optimal combustion phasing. Similarly, knock detection is approached by evaluating two indexes: the distribution of a typical knock-sensitive parameter (MAPO, Maximum Amplitude of Pressure Oscillations) is related to that of CHRNET (net Cumulative Heat Release), determining a robust knock index. A knock limiter controller can then be added, in order to restrict the SA range to safe values. The methodology can be implemented in real-time combustion controllers: the algorithms have been applied offline to sampled data, showing the feasibility of fast and robust automatic mapping procedures.Copyright © 2009 by ASME
Jim Winkelman - One of the best experts on this subject based on the ideXlab platform.
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Wheel slip control for antispin acceleration via dynamic Spark Advance
Control Engineering Practice, 2000Co-Authors: Ibrahim Haskara, Umit Ozguner, Jim WinkelmanAbstract:Abstract One of the problems that might occur during acceleration is wheel spin. While the wheels are spinning, the driving force on the tires reduces considerably and the vehicle cannot speed up as desired. It may even become very difficult to control the vehicle under these conditions. The acceleration characteristics of a vehicle can be improved without changing its physical capabilities with a suitable engine control algorithm that has no additional sensor inputs. This paper presents several control strategies for the wheel slip control problem. The torque output of an engine is modulated to prevent the wheel spin caused by rapid increases in the throttle input. The Spark time is varied dynamically to adjust the engine torque and therefore the problem is often referred to as dynamic Spark Advance (DSA). Variable structure control theory is used for the controller design purposes and simulation results are provided.
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Tuning for dynamic Spark Advance control
Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251), 1999Co-Authors: Jim Winkelman, Ibrahim Haskara, Umit OzgunerAbstract:In this paper, the wheel slip control problem is studied in an engine control framework via dynamic Spark Advance. A baseline dynamic output feedback Spark Advance strategy is further developed to optimize its performance online employing a sliding mode optimization technique with no additional sensor inputs.
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Dynamic Spark Advance Control
IFAC Proceedings Volumes, 1998Co-Authors: Ibrahim Haskara, Umit Ozguner, Jim WinkelmanAbstract:Abstract One of the problems that might occur during acceleration is the wheel slip phenomenon. While the wheels are spinning, the driving force on the tires reduces considerably. The vehicle can not speed up as desired and may become difficult to control. The acceleration characteristics of a vehicle can be improved without changing its physical capabilities with a suitable engine control algorithm. This paper describes and compares different control strategies for the wheel slip limiting problem. The torque output of an engine is modulated to prevent the wheel spin caused by rapid increases in the throttle input. Variable structure control theory is used for the controller design purposes and simulation results are provided.
Lars Eriksson - One of the best experts on this subject based on the ideXlab platform.
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Spark Advance for optimal efficiency
SAE Technical Paper Series, 1999Co-Authors: Lars ErikssonAbstract:Most of todays Spark-Advance controllers operate in open loop but there are several benefits of using feed-back or adaptive schemes based on variables deduced from the cylinder pressure. A systemat ...
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Spark Advance Modeling and Control
1999Co-Authors: Lars ErikssonAbstract:The Spark Advance determines the efficiency of Spark-ignited (SI) engines by positioning the combustion in relation to the piston motion. Today’s Spark-Advance controllers are open-loop systems that measure parameters that effect the SparkAdvance setting and compensate for their effects. Several parameters influence the best Spark-Advance setting but it would be too expensive to measure and account for all of them. This results in a schedule that is a compromise since it has to guarantee good performance over the range of all the non-measured parameters. A closed-loop scheme instead measures the result of the actual Spark Advance and maintains an optimal Spark-Advance setting in the presence of disturbances. To cover this area two questions must be addressed: How to determine if the Spark Advance is optimal and how it can be measured? This is the scope of the present work. One possible measurement is the in-cylinder pressure, which gives the torque, but also contains important information about the combustion. The cylinder pressure can accurately be modeled using well known single-zone thermodynamic models which include the loss mechanisms of heat transfer and crevice flows. A systematic procedure for identifying heat-release model parameters is presented. Three well-known combustion descriptors have been presented in the literature that relate the phasing of the pressure signal to the optimal ignition timing. A parametric study was performed showing how changes in model parameters influence the combustion descriptors at optimum ignition timing. Another possible measurement is the ionization current that uses the Spark plug as a sensor, when it is not used for ignition. This is a direct in-cylinder measurement which is rich in information about the combustion. A novel approach to Spark-Advance control is presented, which uses the ionization current as a sensed variable. The feedback control scheme is closely related to schemes based on incylinder pressure measurements, that earlier have reported good results. A key idea in this approach is to fit a model to the measured ionization current signal, and extract information about the peak pressure position from the model parameters. The control strategy is validated on an SI production engine, demonstrating that the Spark-Advance controller based on ionization current interpretation can control the peak pressure position to desired positions. A new method to increase engine efficiency is presented, by using the closed-loop Spark-Advance control strategy in combination with active water injection. However, the major result is that the controller maintains an optimal Spark Advance under various conditions and in the presence of environmental disturbances such as air humidity.
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Increasing the Efficiency of SI-Engines by Spark-Advance Control and Water Injection
IFAC Proceedings Volumes, 1998Co-Authors: Lars Eriksson, Lars NielsenAbstract:Abstract Engine efficiency can be maximized by directly measuring in-cylinder parameters and adjusting the Spark Advance, using a feedback scheme based on the ionization current as sensed variable. Water injection is shown to increase the engine efficiency, if at the same time the Spark Advance is also changed when water is injected to obtain maximum efficiency. A Spark-Advance control scheme, that takes the water injection into account, is thus necessary to increase the efficiency.
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An ion-sense engine fine-tuner
IEEE Control Systems, 1998Co-Authors: Lars Nielsen, Lars ErikssonAbstract:The paper presents a real-time closed loop demonstration of Spark Advance control by interpretation of ionization current signals. Such control is shown to be able to handle variations in air humidity, which is a major factor influencing burn rates, and consequently pressure buildup and useful work transferred via piston to drive shaft. This leads to a clear improvement in engine efficiency compared to traditional systems using only engine speed and load. Inspired by the type of challenges and potential usefulness in interpretation of ionization current signals, the paper focuses on closed loop ignition control by ionization current interpretation. The topics discussed include: the basics of ionization currents; Spark Advance control, especially principles relating pressure information to efficiency; and the structure of the ion-sense Spark Advance controller. The experimental demonstrations and some conclusions are presented.
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closed loop ignition control by ionization current interpretation
SAE transactions, 1997Co-Authors: Lars Eriksson, Lars Nielsen, Mikael GlaveniusAbstract:The main result of this paper is a real-time closed loop demonstration of Spark Advance control by interpretation of ionization current signals. The advantages of such a system is quantified. The i ...
Mirosław Wendeker - One of the best experts on this subject based on the ideXlab platform.
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Combustion process in a Spark ignition engine: analysis of cyclic peak pressure and peak pressure angle oscillations
Meccanica, 2009Co-Authors: Grzegorz Litak, Jacek Czarnigowski, Tomasz Kamiński, Asok K. Sen, Mirosław WendekerAbstract:In this paper we analyze the cycle-to-cycle variations of peak pressure p _ max and peak pressure angle α _ pmax in a four-cylinder Spark ignition engine. We examine the experimental time series of p _ max and α _ pmax for three different Spark Advance angles. Using standard statistical techniques such as return maps and histograms we show that depending on the Spark Advance angle, there are significant differences in the fluctuations of p _ max and α _ pmax . We also calculate the multiscale entropy of the various time series to estimate the effect of randomness in these fluctuations. Finally, we explain how the information on both p _ max and α _ pmax can be used to develop optimal strategies for controlling the combustion process and improving engine performance.
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Multifractal and statistical analyses of heat release fluctuations in a Spark ignition engine
Chaos (Woodbury N.Y.), 2008Co-Authors: Asok K. Sen, Tomasz Kamiński, Grzegorz Litak, Mirosław WendekerAbstract:Using multifractal and statistical analyses, we have investigated the complex dynamics of cycle-to-cycle heat release variations in a Spark ignition engine. Three different values of the Spark Advance angle (Δβ) are examined. The multifractal complexity is characterized by the singularity spectrum of the heat release time series in terms of the Holder exponent. The broadness of the singularity spectrum gives a measure of the degree of mutifractality or complexity of the time series. The broader the spectrum, the richer and more complex is the structure with a higher degree of multifractality. Using this broadness measure, the complexity in heat release variations is compared for the three Spark Advance angles (SAAs). Our results reveal that the heat release data are most complex for Δβ=30° followed in order by Δβ=15° and 5°. In other words, the complexity increases with increasing SAA. In addition, we found that for all the SAAs considered, the heat release fluctuations behave like an antipersistent or a ...
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Cycle-to-cycle oscillations of heat release in a Spark ignition engine
Meccanica, 2007Co-Authors: Grzegorz Litak, Tomasz Kamiński, Jacek Czarnigowski, Dariusz Żukowski, Mirosław WendekerAbstract:Fluctuations of combustion were studied using experimental time series of internal pressure in one of four cylinders in a Spark ignition engine. Employing standard statistical methods like histograms and return maps, cycle-to-cycle variations of heat release were analyzed. A substantial difference in system behavior corresponding to quality of combustion was observed with a changing Spark Advance angle. Examining recurrence plots for a higher Spark Advance angle formation of specific patterns of vertical lines characteristic to intermittent behavior was found.
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Combustion process in a Spark ignition engine: Dynamics and noise level estimation
Chaos (Woodbury N.Y.), 2004Co-Authors: Tomasz Kamiński, Mirosław Wendeker, Krzysztof Urbanowicz, Grzegorz LitakAbstract:We analyze the experimental time series of internal pressure in a four cylinder Spark ignition engine. In our experiment, performed for different Spark Advance angles, apart from the usual cyclic changes of engine pressure we observed additional oscillations. These oscillations are with longer time scales ranging from one to several hundred engine cycles depending on engine working conditions. Based on the pressure time dependence we have calculated the heat released per combustion cycle. Using the time series of heat release to calculate the correlation coarse-grained entropy we estimated the noise level for internal combustion process. Our results show that for a larger Spark Advance angle the system is more deterministic.
