Observer Based Cylinder Charge Estimation for Spark-ignition Engines

Internal combustion engines require accurate cylinder charge estimation for determining engine torque, controlling air-to-fuel ratio (AFR), and ensuring high after-treatment efficiency. This is challenging due to the highly transient operating conditions that are common in automobile engines. The problem is further complicated by spark ignition (SI) engine technologies such as variable valve timing (VVT) and exhaust gas recirculation (EGR) which are applied to improve fuel economy and reduce pollutant emissions. With manifold filling/emptying/mixing phenomenon and different actuator response times, these technologies significantly increase the complexity of cylinder charge estimation. Current cylinder charge estimation methodologies require a combination of sensors and empirical models to deal with the high degrees of control freedom existent on the engine. But these methods have the drawbacks of great dependency on accurate calibration and poor transient performance. Most importantly, the current methods isolate feed-forward cylinder charge estimation and feedback AFR control. When there is discrepancy between target lambda value and sensed lambda value at exhaust side, the current control/estimation method will trim the fuel injection amount no matter where the error source is. As a matter of fact, the error might come from the throttle flow estimation, the fuel injection flow estimation, EGR flow estimation, or any combination of these error sources. Increased air-path complexity and drawbacks of traditional methods drive the need for cost effective solutions that produce high air/EGR/fuel charge estimation accuracy with the ability to identify the error source while minimizing sensor cost, computational effort, and calibration time. This research first evaluates the existing work on air charge estimation for SI engines with massive experimental tests covering various operating conditions, which are designed for the algorithm verification of this research. Then several estimation methods which utilize both Manifold Absolute Pressure (MAP) and Mass Air Flow (MAF) sensors are studied and analyzed. Reduction of calibration effort and improvement of accuracy are observed from the proposed cylinder air charge estimation methods. Following that, a model is built to study the engine gas path dynamics and characteristics and then simplified to provide system dynamic basis for the following estimation algorithm development. Using the developed model, a disturbance observer based cylinder charge estimation technique is developed based on a combination of sensors including MAF, MAP, and exhaust lambda sensors. This developed algorithm significantly improves engine states estimation accuracy compared to conventional Single-Input-Single-Output (SISO) methods. Also, the augmentation of disturbance observation is able to pin point the source of the estimation error. Through experimental validation, using the developed estimation method with proper parameters, the error source of estimation can be identified and rectified when disturbance is introduced to throttle flow model, EGR flow model, fuel injection flow model or any combination of these models. The structure of the proposed algorithm should adapt to most SI engine configurations. It can help the engine controller to mitigate modeling errors thus improve the performance of physics model based engine control especially AFR control

In-cylinder pressure resonance analysis for trapped mass estimation in automotive engines

This thesis presents a new application for in-cylinder pressure sensors in internal combustion engines. The new method takes profit of the high-frequency content of the in-cylinder pressure signal to determine the speed of sound evolution during the expansion stroke and combines this estimation with the low-frequency content of the pressure signal and a volume estimation to obtain a measurement of the trapped mass. The new method is based on the studies of the resonance phenomenon in pent-roof combustion chambers and proposes three calibration procedures to determine the resonant frequency evolution when bowl-in-piston geometries are considered. The Fourier transform has been modified in order to include harmonics with frequency variations, which allows a rapid identification of the resonant modes with no need of time-frequency analysis, e.g. STFT or WD. The main limitation of the method resides in the resonance excitation, which may be insufficient in low-load conditions, such as idle. An observer is presented to overcome that problem. The observer takes into account the dynamics of the sensors, the dynamics at the intake manifold, and combines current flow sensors with intermittent measurements, such as the trapped mass obtained by the resonance method, to provide the system with accurate and robust measurements of the trapped mass, the EGR, and the composition at the exhaust. The trapped mass obtained by the resonance method has been compared with auxiliary methods in various experimental facilities: in a SI engine, where no EGR exist, the differences founded were below 1%, in a conventional CI light-duty engine the average of the differences over 808 operating conditions accounted for a 2.64 %, in a research heavy-duty RCCI engine, with EGR, port fuel gasoline, and direct diesel injections, the average difference was 2.17 %, and in a research two-strokes single cylinder engine, where significant short-circuit and residual gases exist, the differences founded were 4.36 %. In all the studied cases the differences founded with the reference estimation can be attributed to the auxiliary method employed and its expected error. In order to demonstrate the potential of the resonance method four applications for control and diagnosis of internal combustion engines have been proposed: the estimation of residuals in engines with NVO, the prediction of knock in SI engines, the estimation of the exhaust gases temperature, and a NOx model for CI engines. In the four applications the method was compared with current methodologies and with additional sensors, demonstrating the improvement in accuracy and a cycle-to-cycle resolution.Esta tesis presenta una nueva aplicación para los sensores de presión en cámara. El nuevo método utiliza el contenido de alta frecuencia de la señal de presión en cámara para estimar la evolución de la velocidad del sonido durante la expansión de los gases de escape y combina esta estimación con el contenido de baja frecuencia de la presión en cámara y el volumen instantáneo de la cámara para obtener una medida de la masa atrapada. El nuevo método está basado en los estudios de la resonancia en cámaras de combustión cilíndricas y propone tres procedimientos de calibración para estimar la evolución de la frecuencia de resonancia en cámaras de combustión con bowl. La transformada de Fourier ha sido modificada para considerar harmónicos con frecuencias que varían en el tiempo, lo que permite una rápida identificación de los modos de resonancia sin necesidad de utilizar un análisis en tiempo frecuencia, como por ejemplo STFT o WD. La principal limitación del método es la necesidad de excitación suficiente de la resonancia, que puede impedir su uso en condiciones de baja carga como el ralentí. Para solventar este problema se ha diseñado un observador. El observador incluye las dinámicas de los sensores, las dinámicas del colector de admisión, y combina los sensores actuales de flujo con medidas intermitentes (como la medida ofrecida por el nuevo método de la resonancia) para obtener medidas de la masa atrapada, del EGR y de la composición en el escape precisas y robustas. La medida de la masa atrapada obtenida por el método de la resonancia ha sido comparado con métodos auxiliares en diferentes instalaciones experimentales: en un motor SI, sin EGR, las diferencias con los sensores eran menores del 1%, en un motor convencional CI la media de las diferencias sobre 808 puntos de operación distintos ha sido de 2.64 %, en un motor de investigación con EGR, con inyección gasolina en el colector e inyección directa de diesel, las diferencias fueron de 2.17 %, y en un motor de investigación de dos tiempos, donde existían grandes cantidades de corto-circuito y gases residuales, las diferencias fueron de 4.36 %. En todos los casos estudiados las diferencias encontradas pueden ser atribuidas a los errores que caracterizan los métodos auxiliares utilizados para obtener la medida de referencia. Finalmente, para demostrar el potencial del método se han desarrollado cuatro aplicaciones para control y diagnóstico de motores de combustión interna alternativos: la estimación de gases residuales en motores con NVO, la predicción de knock en motores SI, la estimación de la temperatura de los gases de escape, y un modelo de NOx para motores CI. En las cuatro aplicaciones el método ha sido comparado con los sistemas de medidas actuales y con sensores adicionales, demostrando mejoras importantes en la precisión de la medida y una resolución de un solo ciclo.Aquesta tesi presenta una nova aplicació per als sensors de pressió en cambra. El nou mètode utilitza el contingut d'alta freqüència del senyal de pressió en cambra per estimar l'evolució de la velocitat del so durant l'expansió dels gasos d'eixida i combina aquesta estimació amb el contingut de baixa freqüència de la pressió en cambra i el volum instantani de la cambra per obtenir una mesura de la massa atrapada. El nou mètode està desenvolupat dels estudis de la ressonància en cambres de combustió cilíndriques i proposa tres procediments de calibratge per estimar l'evolució de la freqüència de ressonància en cambres de combustió amb bowl. La transformada de Fourier ha sigut modificada per considerar harmònics amb freqüències que varien en el temps, el que permet una ràpida identificació dels modes de ressonància sense necessitat d'utilitzar una anàlisi en temps-freqüència, com per exemple la STFT o la WD. La principal limitació del mètode és la necessitat d'excitació suficient de la ressonància, que pot impedir el seu ús en condicions de baixa càrrega, com al ralentí. Per solucionar aquest problema s'ha desenvolupat un observador. L'observador inclou les dinàmiques dels sensors, les dinàmiques del col·lector d'admissió, i combina els sensors actuals de flux amb mesures intermitents (com l'obtinguda pel nou mètode de la ressonància) per obtenir mesures de la massa atrapada, del EGR i de la composició d'eixida precises i robustes. La mesura de la massa atrapada obtinguda pel mètode de la ressonància ha sigut comparada en mètodes auxiliars en diferents instal·lacions experimentals: a un motor SI, sense EGR, les diferencies amb els sensors estaven per davall de l'1 %, a un motor convencional CI la mitja de les diferències sobre 808 punts d'operació diferents ha sigut de 2.64 %, a un motor d'investigació, en EGR, en injecció gasolina en el col·lector i injecció directa de dièsel, les diferències van ser de 2.17 %, i a un motor d'investigació de dos temps, on existien grans quantitats de curtcircuit i residuals, les diferencies foren de 4.36 %. En tots els casos estudiats les diferències trobades poden ser atribuïdes als errors que caracteritzen els mètodes auxiliars utilitzats per obtenir la mesura de referència. Finalment, per demostrar el potencial del mètode s'han desenvolupat quatre aplicacions per al control i diagnòstic de motors de combustió interna alternatius: l'estimació de gasos residuals en motors amb NVO, la predicció de knock en motors SI, l'estimació de la temperatura dels gasos d'eixida, i un model de NOx per a motors CI. En les quatre aplicacions el mètode ha sigut comparat amb els sistemes de mesures actuals i amb sensors addicionals, demostrant millores importants en la precisió de la mesura i una resolució de solament un cicle.Bares Moreno, P. (2017). In-cylinder pressure resonance analysis for trapped mass estimation in automotive engines [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/9042

Integration of intermittent measurement from in-cylinder pressure resonance in a multi-sensor mass flow estimator

[EN] A novel technique of trapped mass determination, based on the in-cylinder pressure resonance, has been recently published by the authors. However, the method only works when sufficient resonance intensity exists and the current formulation might preclude its implementation in real-time due to excessive computational burden. The present paper proposes an iterative algorithm for reducing the number of operations, an adaptive filter to identify faulty measurements and a Kalman filter that combines several sensors and models, currently used in commercial light-duty engines, to ensure a continous estimation of trapped mass, air mass, and exhaust gas recirculation (EGR). The filter is implemented using experimental data of a EURO 6 light-duty engine in a world harmonize light-duty test cycle (WLTC), showing the potential of being implemented in real driving conditions with robustness and harnessing a new measurement to improve the accuracy and response of current estimations.Guardiola, C.; Pla Moreno, B.; Bares-Moreno, P.; Peyton Jones, J. (2019). Integration of intermittent measurement from in-cylinder pressure resonance in a multi-sensor mass flow estimator. Mechanical Systems and Signal Processing. 131:152-165. https://doi.org/10.1016/j.ymssp.2019.05.052S15216513

Parameter Identification of Nonlinear System on Combustion Engine Based MVEM using PEM

In four-stroke engine injection system, often called spark ignition (SI) engine, the air-fuel ratio (AFR) is taken from the measurement of lambda sensor in the exhaust. This sensor does not directly describe how much AFR in the combustion chamber due to the large transport delay. Therefore, the lambda sensor is used only as a feedback in AFR control "correction", not as the "main" control. The purpose of this research is to identify the parameters of the non-linear system in SI engines to produce AFR estimator. The AFR estimator is expected to be used as a feedback of the main "AFR" control system. The process of identifying the parameters using the Gauss-Newton method, due to its rapid computation to Achieve convergence, is based on prediction error minimization (PEM). The models of AFR estimator is an open-loop system without a universal exhaust gas oxygen (UEGO) sensors as feedback, called a virtual AFR sensor. The high price of UEGO sensors makes the virtual AFR sensor can be a practical solution to be applied in AFR control. The model in this research is based on the mean value engine models (MVEM) with some modifications. The research dataset was taken from a Hyundai Verna 2002 with the additional UEGO type of lambda sensors. The throttle opening angle (input) is played by stepping on the gas pedal and the signal to the injector (input) is set to a certain quantity to produce the AFR (output) value read by the UEGO sensor. This research produces an open loop estimator model or AFR virtual sensors with normalized root mean square error (NRMSE) = 0.06831 = 6.831%

Data driven nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions

Diesel engines' reliable and durable structures, high torque generation capabilities at low speeds, and fuel consumption efficiencies make them irreplaceable for heavy-duty vehicles in the market. However, ine ciencies in the combustion process result in the release of emissions to the environment. In addition to the restrictive international regulations for emissions, the competitive demands for more powerful engines and increasing fuel prices obligate heavy-duty engine and vehicle manufacturers to seek for solutions to reduce the emissions while meeting the performance requirements. In line with these objectives, remarkable progress has been made in modern diesel engine systems such as air handling, fuel injection, combustion, and after-treatment. However, such systems utilize quite sophisticated equipment with a large number of calibratable parameters that increases the experimentation time and effort to find the optimal operating points. Therefore, a dynamic model-based transient calibration is required for an e cient combustion optimization which obeys the emission limits, and meets the desired power and efficiency requirements. This thesis is about developing optimizationoriented high delity nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions. Contributions of the thesis are: (i) A new design of experiments is proposed where air-path and fuel-path input channels are excited by chirp signals with varying frequency pro les in terms of the number and directions of the sweeps. The proposed approach is a strong alternative to the steady-state experiment based approaches to reduce the testing time considerably and improve the modeling accuracy in both steady-state and transient conditions. (ii) A nonlinear nite impulse response (NFIR) model is developed to predict indicated torque by including the estimations of friction, pumping and inertia torques in addition to the torque measured from the engine dynamometer. (iii) Two different nonlinear autoregressive with exogenous input (NARX) models are proposed to predict NOx emissions. In the first structure, input regressor set for the nonlinear part of the model is reduced by an orthogonal least square (OLS) algorithm to increase the robustness and decrease the sensitivity to parameter changes, and linear output feedback is employed. In the second structure, only the previous output is used as the output regressor in the model due to the stability considerations. (iv) An analysis of model sensitivities to parameter changes is conducted and an easy-tointerpret map is introduced to select the best modeling parameters with limited testing time in powertrain development. (v) Soot (particulated matter) emission is predicted using LSTM type networks which provide more accurate and smoother predictions than NARX models. Experimental results obtained from the engine dynamometer tests show the e ectiveness of the proposed models in terms of prediction accuracies in both NEDC (New European Driving Cycle) and WHTC (World Harmonized Transient Cycle) cycle

AIR CHARGE ESTIMATION FOR AN SI ENGINE USING IN-CYLINDER PRESSURE SENSOR

An accurate estimation of cycle by cycle in-cylinder mass and the composition of the combustion chamber charge is required for engine control strategies to meet stringent pollution emission and fuel consumption regulations. Estimation of fresh charge and residual gas masses is beneficial in terms of fuel efficiency, tailpipe emissions, engine performance, for engine control strategies. Air-flow meter, which is mounted in the intake air circuit, can be utilized in a closed-loop strategy to control air charge. However, air flow meter has a response delay; moreover dynamics of intake manifold and pipes must be taken into consideration to improve the estimation of air charge and accurate feedback in transients. As an alternative to air flow meter, in-cylinder pressure sensors can be utilized to directly measure cylinder pressure, based on which, the amount of air charge can be estimated without the requirement to model the dynamics of the manifold. In this work, an air charge estimation algorithm is proposed, which uses cylinder pressure trace data at specific cycle events, and by applying thermodynamics and heat transfer relationships, estimates individual cylinder air charge for each cycle in different test conditions. A residual gas estimator, which can be applied online, is also incorporated in the algorithm to estimate residual gas mass for each cycle. Estimator output is validated and calibrated based on experimental setup air charge, which is calculated from the amount of injected fuel in each cylinder and individual wide-band sensor data. Uncertainty propagation analysis is performed to investigate the uncertainty in estimated air charge based on the uncertainties in measured and model parameters. This analysis reveals the information about the parameters with major contribution to the uncertainty in estimated air charge

Observer-based engine air charge characterisation: rapid, observer-assisted engine air charge characterisation using a dynamic dual-ramp testing method

Characterisation of modern complex powertrains is a time consuming and expensive process. Little effort has been made to improve the efficiency of testing methodologies used to obtain data for this purpose. Steady-state engine testing is still regarded as the golden standard, where approximately 90% of testing time is wasted waiting for the engine to stabilize. Rapid dynamic engine testing, as a replacement for the conventional steady-state method, has the potential to significantly reduce the time required for characterisation. However, even by using state of the art measurement equipment, dynamic engine testing introduces the problem that certain variables are not directly measurable due to the excitation of the system dynamics. Consequently, it is necessary to develop methods that allow the observation of not directly measurable quantities during transient engine testing. Engine testing for the characterisation of the engine air-path is specifically affected by this problem since the air mass flow entering the cylinder is not directly measurable by any sensor during transient operation. This dissertation presents a comprehensive methodology for engine air charge characterisation using dynamic test data. An observer is developed, which allows observation of the actual air mass flow into the engine during transient operation. The observer is integrated into a dual-ramp testing procedure, which allows the elimination of unaccounted dynamic effects by averaging over the resulting hysteresis. A simulation study on a 1-D gas dynamic engine model investigates the accuracy of the developed methodology. The simulation results show a trade-off between time saving and accuracy. Experimental test result confirm a time saving of 95% compared to conventional steady-state testing and at least 65% compared to quasi steady-state testing while maintaining the accuracy and repeatability of conventional steady-state testing

Review of sensing methodologies for estimation of combustion metrics

For reduction of engine-out emissions and improvement of fuel economy, closed-loop control of the combustion process has been explored and documented by many researchers. In the closed-loop control, the engine control parameters are optimized according to the estimated instantaneous combustion metrics provided by the combustion sensing process. Combustion sensing process is primarily composed of two aspects: combustion response signal acquisition and response signal processing. As a number of different signals have been employed as the response signal and the signal processing techniques can be different, this paper did a review work concerning the two aspects: combustion response signals and signal processing techniques. In-cylinder pressure signal was not investigated as one of the response signals in this paper since it has been studied and documented in many publications and also due to its high cost and inconvenience in the application