Closed-loop Combustion Control Research Articles

An analytical approach to converting vibration signal to combustion characteristics of homogeneous charge compression ignition engines

Homogeneous charge compression ignition (HCCI) is a promising low-temperature combustion technique for low-emission internal combustion engines. Unlike conventional engines, HCCI lacks a direct ignition control mechanism, necessitating closed-loop combustion control. This study proposes a phenomenological-based, cost-effective, and non-intrusive approach using vibration data analysis to determine essential combustion parameters. Experiments were conducted on a single-cylinder research engine with an accelerometer attached to the engine head. The engine operation envelope covered the whole engine’s operating area in naturally aspirated HCCI mode. Wavelet analysis revealed that combustion-related frequencies centered around 500 Hz, independent of operating conditions. The correlation-seeking analysis included peak acceleration amplitude and its crank angle with peak heat release rate (HRR) data. The peak HRR location was accurately identified within one degree when vibration amplitude exceeded the 100 m/s2 threshold. This encompassed 98.5% of the analyzed combustion cycles. The peak HRR prediction accuracy had a maximum error below 21% and was suitable to monitor reaction rates, especially in incomplete combustion and high ringing cycles.

Advanced model-based closed-loop combustion control strategies with combustion rate shaping

Combustion rate shaping (CRS) is an advanced closed-loop concept which controls the crank-angle resolved combustion trace by adapting the fuel injection profile. The thermodynamic potential of this concept to enable real-time combustion control independent of fuel property variations, injector drift, cylinder tolerances, or changing air-path boundary conditions has already been demonstrated. The highest benefit of this control concept is achieved when it is directly applied on the engine control unit (ECU) in combination with an in-cylinder pressure measurement. This enables online optimization and adaption of the fuel- and air-path settings to the fuel used during vehicle operation. Therefore, the development of a CRS concept with real-time capability has been the main focus so far. To ensure a robust online real-time combustion control, a key development step was the identification of control variables that describe the entire combustion process and only minimally influence each other. For this purpose, the combustion phasing, the combustion gradient and the indicated mean effective pressure have been determined as parameters for the feedback control. However, the stringent requirements for higher efficiencies, emission robustness and combustion noise necessitate the extension of the CRS control concept. Therefore, in this work, the CRS control concept is extended with the aim of realizing a combustion with constant volume pressure increase to the peak firing pressure limit to achieve highest efficiency. In addition, intelligent control of the fuel injection profile is to be used to achieve a combustion temperature for lowest possible NOx raw emissions and a robust combustion noise excitation limitation. The optimized control strategy is implemented on a heavy-duty single cylinder engine test bench using a Rapid Control Prototyping (RCP) system, and the control performance is demonstrated.

An Artificial Intelligence Regression Model for Prediction of NOx Emission from Flame Image

In this study, NOx emission has been estimated by processing the flame image of visible wavelength and its experimental verification has been presented. The experimental study has been performed by using a domestic coal boiler with a capacity of 85000 Kcal / h. The real NOx value has been measured from a flue gas analyzer device. The flame image has been taken by CCD camera from the observation hole on the side of the burner. The data set which is related to instantaneous combustion performance and flame images was recorded simultaneously on the same computer with time stamps once a second. The color flame image has been transformed into a gray scale. Features have been extracted from the gray image of flame. The features are extracted by using the cumulative projection vectors of row and column matrices. ANN regression model has been used as the learning model. The relationship between flame image and NOx emission has been obtained with the accuracy of R = 0.9522. Highly accurate measurement results show that the proposed NOx prediction model can be used in combustion monitor and control systems.

Accelerometer-based SOC estimation methodology for combustion control applied to Gasoline Compression Ignition

The European Community’s recent decision to suspend the marketing of cars with conventional fossil-fueled internal combustion engines from 2035 requires new solutions, based on carbon-neutral technologies, that ensure equivalent performances in terms of reliability, trip autonomy, refueling times and end-of-life disposal of components compared to those of current gasoline or diesel cars. The use of bio-fuels and hydrogen, which can be obtained by renewable energy sources, coupled with high-efficiency combustion methodologies might allow to reach the carbon neutrality of transports (net-zero carbon dioxide emissions) even using the well-known internal combustion engine technology. Bearing this in mind, experiments were carried out on compression ignited engines running on gasoline (GCI) with a high thermal efficiency which, in the future, could be easily adapted to run on a bio-fuel. Despite the well-reported benefits of GCI engines in terms of efficiency and pollutant emissions, combustion instability hinders the diffusion of these engines for industrial applications. A possible solution to stabilize GCI combustion is the use of multiple injections strategies, typically composed by 2 early injected fuel jests followed by the main injection. The heat released by the combustion of the earlier fuel jets allows to reduce the ignition delay of the main injection, directly affecting both delivered torque and center of combustion. As a result, to properly manage GCI engines, a stable and reliable combustion of the pre-injections is mandatory. In this paper, an estimation methodology of the start of combustion (SOC) position, based on the analysis of the signal coming from an accelerometer sensor mounted on the engine block, is presented (the optimal sensor positioning is also discussed). A strong correlation between the SOC calculated from the accelerometer and that obtained from the analysis of the rate of heat release (RoHR) was identified. As a result, the estimated SOC could be used to feedback an adaptive closed-loop combustion control algorithm, suitable to improve the stability of the whole combustion process.

Development of a Control-Oriented Ignition Delay Model for GCI Combustion

Increasingly stringent pollutant emission limits and CO2 reduction policies are forcing the automotive industry toward cleaner and decarbonized mobility. The goal is to achieve carbon neutrality within 2050 and limit global warming to 2 °C (possibly 1.5 °C) with respect to pre-industrial levels as stated in both the European Green Deal and the Paris Agreement and further reiterated at the COP26. With the aim of simultaneously reducing both pollutants and CO2 emissions, a large amount of research is currently carried out on low-temperature highly efficient combustions (LTC). Among these advanced combustions, one of the most promising is Gasoline Compression Ignition (GCI), based on the spontaneous ignition of a gasoline-like fuel. Nevertheless, despite GCI proving to be effective in reducing both pollutants and CO2 emissions, GCI combustion controllability represents the main challenge that hinders the diffusion of this methodology for transportation. Several works in the literature demonstrated that to properly control GCI combustion, a multiple injections strategy is needed. The rise of pressure and temperature generated by the spontaneous ignition of small amounts of early-injected fuel reduces the ignition delay of the following main injection, responsible for the torque production of the engine. Since the combustion of the pre-injections is chemically driven, the ignition delay might be strongly affected by a slight variation in the engine control parameters and, consequently, lead to misfire or knocking. The goal of this work was to develop a control-oriented ignition delay model suitable to improve the GCI combustion stability through the proper management of the pilot injections. After a thorough analysis of the quantities affecting the ignition delay, this quantity was modeled as a function of both a thermodynamic and a chemical–physical index. The comparison between the measured and modeled ignition delay shows an accuracy compatible with the requirements for control purposes (the average root mean squared error between the measured and estimated start of combustion is close to 1.3 deg), over a wide range of operating conditions. As a result, the presented approach proved to be appropriate for the development of a model-based feed-forward contribution for a closed-loop combustion control strategy.

Study on the closed-loop combustion control for a diesel engine by using a dynamic-target online prediction model

In-cylinder pressure-based closed-loop combustion control is a critical technology used to address the issue of cylinder-to-cylinder inhomogeneity throughout the life cycle of a modern diesel engine. Nevertheless, closed-loop combustion control technology (CLCC), which requires a reference or set-point that the control attempts to achieve, causes an increase in the operating maps and the complexity of the calibration effort. In this study, a reference governor-based combustion control (RGCC) is developed in order to reduce the cylinder-to-cylinder combustion dispersions and the calibration effort associated with enabling of the CLCC simultaneously. The RGCC consists of a dynamic-target online prediction model and an indicated mean effective pressure (IMEP) control strategy. The dynamic-target prediction model is designed based on the IMEP of the engine real-time tests; it uses a modified Kalman filter algorithm, which provides the set-point value for the IMEP control strategy. The effectiveness of the proposed RGCC is experimentally validated on a commercial diesel engine test bench under steady-state and transient-state conditions. The experimental results show that the RGCC displayed far better transient tracking performance than the closed-loop IMEP control based on MAP. Furthermore, the controller can effectively reduce cylinder-to-cylinder IMEP variations under steady-state and transient-state conditions.

Indicated efficiency optimization by in-cycle closed-loop combustion control of diesel engines

This paper revises the design process for closed-loop combustion control based on in-cycle feedback. Methods for the necessary observers, detectors and virtual sensors are also discussed. The control problem tackles the indicated efficiency optimization, following two approaches, direct and indirect optimization. The argumentation provides a comprehensive overview on how to specify and integrate different methods available at the literature. The results presented are based on previous experimental and simulation work on a Scania D13 engine by the authors. A pilot and main injection scheme is used to increase the degrees of freedom and achieve a higher nominal indicated efficiency. An in-cycle controllability study indicated that the pilot mass is the most significant parameter to monitor for the in-cycle disturbance rejection. Based on the heat release analysis, feedback is obtained from the pilot mass estimation, with a real-time accuracy of ±0.5 mg/st. Still, to overcome the inherent delay between feedback and control action, predictive models are necessary. The required accuracy can be obtained by the multi-cylinder adaptation of the model parameters. The paper presents two approaches for the design of in-cycle controllers to improve the indicated efficiency under hardware and emission constraints. In the direct efficiency optimization, a model predicts the indicated efficiency change from the in-cycle pilot estimations to optimize the main fuel injection. Compared to in-cycle open-loop operation, the in-cycle controller increases the indicated efficiency by +0.42%unit. In the indirect efficiency optimization, the controller is designed to reduced the cyclic dispersion of the pilot SOC (down to ±0.4CAD), the pilot burnt mass (down to ±0.6 mg), the main SOC (down to ±0.3CAD) and the IMEP (down to ±0.2 bar). In case pilot misfire is detected, the CA50 shift of +2.6CAD is compensated and reduced to +0.5CAD. The indicated efficiency is maximized by the set-point optimization in a Monte Carlo simulation of a stochastic combustion model that captures the reduced cyclic dispersion of the controller. This approach increased the indicated efficiency by +0.6%unit under constraints on the maximum pressure, the maximum pressure rise rate, the maximum and the minimum exhaust temperature.Despite the controller effectiveness was quantified experimentally, the simulation results must be validated experimentally as well. Further investigations can focus on including constraints on emissions, how to handle the constraints on-line, the inclusion of additional injection pulses in the optimization, as well as variable valve timing actuation or injection of emissions reductants.

Smart in-cylinder pressure sensor for closed-loop combustion control

Abstract. Our approach of a closed-loop combustion control is built on an intensively evaluated robust cylinder pressure sensor with integrated smart electronics and an openly programmed engine control unit. The presented pressure sensor consists of a steel membrane and a highly strain-sensitive thin film with laser-welded electrical contacts. All components are optimized for reliable operation at high temperatures. The sensor setup safely converts the in-cylinder pressure of a combustion engine at temperatures of up to 200 ∘C into the desired electrical values. Furthermore, the embedded smart electronics provides a fast analogue to digital conversion and subsequently computes significant combustion parameters in real time, based on implemented thermodynamic equations, namely the 50 % mass fraction burned, the indicated mean effective pressure, the maximum pressure and a digital value, which represents the intensity of knocking. Only these aggregated parameters – not the running pressure values – are sent to the engine control unit. The data communication between the smart sensor and the engine control unit is based on the controller area network bus system, which is widely spread in the automotive industry and allows a robust data transfer minimizing electrical interferences. The established closed-loop combustion control is able to control the ignition angle in accordance with the 50 % mass fraction burned at a certain crankshaft angle. With this loop, the combustion engine is controlled and run efficiently even if the ignition angle is intentionally incorrectly adjusted. The controlled and automatic correction of simulated ageing effects is demonstrated as well as the self-adjustment of an efficient operation when different fuels are used. In addition, our approach saves the computing capacity of the engine control unit by outsourcing the data processing to the sensor system.

A Model-based Method of Monitoring Combustion Pressure Measurement Chains for Closed-loop Combustion Control Applications

Significant research is ongoing into the use of carbon neutral fuels for combustion engines. In order to fully exploit these new fuel technologies, alongside existing carbon based fuel types, the engine requires a sophisticated level of control for the combustion event based upon a cyclic feedback loop (i.e., a preceding cycle provides the optimization data for the following cycle). This then allows the engine and combustion controller to have the capability to respond and optimise, normal and abnormal combustion phenomena, based on information gained during run-time (as opposed to inferred or indirectly derived parameters). An essential element is the in-cylinder pressure sensor and its measuring chain, that supplies the raw in-cylinder pressure curve for real time analysis within the control system. This paper describes a model-based method for monitoring such a dynamic system, in which the condition of the physical system is represented in a dynamic model consisting of the engine itself, plus the measuring chain, as coupled models. The intent is to mimic the condition of the complete physical system in the model, allowing comparison of model and physical systems to observe residuals and identify fault conditions with all, or part of the measuring chain components.

Combustion parameter evaluation of diesel engine via vibration acceleration signal

Efficient combustion control has increasingly become a quality requirement for automobile manufacturers because of its impact on pollutant and greenhouse gas emissions. In view of this, the management system development of modern internal combustion engines is mainly aimed at combustion control. The real-time detection of in-cylinder pressure characteristic parameters has a considerable significance on the closed-loop combustion control of the internal combustion engine. This paper presents a detection method in which the start of combustion, peak pressure, maximum pressure rise rate, and phase of maximum pressure rise rate are identified through vibration acceleration signal. In order to analyze the relationship between vibration and in-cylinder pressure signal, experimental data are acquired in a diesel engine by implementing various injection strategies and engine operating conditions (speed and load). The results show that the start of combustion can be detected by analyzing its relationship with the peak position of the filtered vibration signal, and the phase of the maximum pressure rise rate can be identified by examining its relationship with the zero-cross position that is adjacent to the right of the peak position. Moreover, the filtered vibration signals are also truncated in the same length and utilized as inputs for algorithms to detect the peak pressure and the maximum pressure rise rate. The algorithms are mainly performed on data compression (or feature extraction) and target regression. Major algorithms, such as one-dimensional convolutional neural network, compression sensing, wavelet decomposition, multilayer perceptron, and support vector machine, are tested. Various experimental results verify that for the test engine the phase detection accuracy of the start of combustion and maximum pressure rise rate is less than 1.7°CA for a 95% prediction interval width. For the detection of the peak pressure and maximum pressure rise rate, the normalized error threshold is set as 0.05, then the accuracies can be not less than 95%.

Combustion phase identification for closed-loop combustion control by resonance excitation in marine diesel engines

In order to improve the energy efficiency and suppress the environmental pollutants of marine diesel engines, analysis and control of combustion phases including late combustion phase and premixed combustion phase are considered as an attractive potential strategy. Consequently, it is necessary to accurately identify the combustion parameters that are representative to these combustion phases, such as start of combustion (SOC), start of diffusive combustion (SODC) and start of late combustion (SOLC). The real-time analysis of the combustion phases can be further applied for closed-loop combustion control (CLCC). However, the existing methods can’t guarantee the accurate detection of combustion parameters simultaneously even though high computational resources are invested. In this study, the resonance excitation of in-cylinder pressure was found to be capable for identifying all of the three combustion phases parameters. Hence, several effective signal processing techniques were proposed to detect these combustion phases parameters from resonant pressure signal. Without complex mathematic algorithms, the proposed method shows high potential for CLCC since it is relatively easier and faster to achieve than other existing techniques. The accuracy of this method was validated by experimental heat release rate and in-cylinder temperature. Furthermore, based on combustion phase parameters obtained, the estimations of combustion state and energy efficiency were discussed. Finally, this method was also applied for combustion phase detection in another type of marine diesel engine, which verified the robustness of this method.

Experimental study of the combustion characteristics prediction model for a sensor-less closed-loop control in a heavy-duty NG engine

The closed-loop combustion control is an effective way to reduce the cycle-to-cycle variations of spark ignition (SI) engine. In this paper, a series of experimental tests were conducted to investigate the combustion performance and emissions characteristics in a six-cylinder natural gas (NG) engine. The correlation between combustion phasing and control parameters was analyzed under different EGR rates and spark timing (ST) at medium and low loads. Additionally, a prediction model on the basis of the correlation was developed employing artificial neural network (ANN) for a sensor-less closed-loop combustion control. After analyzing the results, it is found that a linear relationship exists between CA 50 and ST under various EGR rates. Engine out emissions (i.e., CO, THC, and CH4) are insensitively affected by the CA50. It is observed that the NOx emission is significantly higher in comparison to those under the higher EGR rate. As a consequence, CA50 is acting as the feedback signal for closed-loop combustion control with consideration of cylinder consistency. Moreover, the ANN model was proven to be an impressive way to predict the combustion characteristics, which can substitute the cylinder pressure sensor. Moreover, the prediction model can be applied in a sensor-less closed-loop combustion control, while the correlation coefficient value can reach up to approximately 1.00.

Adaptive in-cylinder pressure model for spark ignition engine control

In-cylinder pressure is the most important variable to analyze the combustion process in internal combustion engines, and can be used as feedback signal for closed-loop combustion control and diagnostics. However, pressure sensors are still affected by challenges such as durability and cost, which prevent their use in mass-production vehicles. Therefore, this work presents a model-based approach to estimate the in-cylinder pressure by means of the combination of a control oriented model and information from the set of sensors available in current production engines for automotive applications. Pressure peak location of each cylinder is estimated through the knock sensor signal, and used as feedback to improve the model. An extended Kalman filter is used to adapt the model to the information from the knock sensor signal. The adaptive model is implemented in a four cylinder light-duty engine and compared with the open loop model, ensuring a continuous estimation of in-cylinder pressure signal, and an improvement for the estimation of different cycle by cycle combustion parameters and cylinder to cylinder variations. Finally, the proposed approach is applied with different fuels showing that the proposed method can be applied independently on the fuel used.

Analysis of ion current signal during negative valve overlap of HCCI combustion with high compression ratio

Homogenous charge compression ignition (HCCI) combustion is a low temperature combustion process which combines high combustion efficiency with ultra-low [Formula: see text] raw emissions. Steep increases of the in-cylinder pressure and unstable combustion sequences at the limits of the operating range can damage the engine and limit the use of HCCI to part load operation. This can be done using closed loop combustion control based on combustion parameters like the indicated mean effective pressure and the combustion phasing. Since in-cylinder pressure sensors are expensive components and therefore not suitable for series application, ion current sensors can be used as an additional source of information about the combustion. Combustion analysis using methods similar to those used in pressure based measurements can be implemented using an online analysis of the ion current signal. In this study, the ion current sensor will be examined for its suitability for combustion control under HCCI conditions with lean air/fuel ratios and high compression ratios. Research has found that the ion current signal is strongly depended on the boundary conditions. Especially the air/fuel ratio which plays an important role for signal strength during the combustion process. When using valve timings with negative valve overlap in combination with a fuel pre-injection, a further peak of the ion current signal close to the gas exchange top dead center can be found in addition to the one during combustion. At the same time, it is hard to extract information from the cylinder pressure signal during NVO. Under lean conditions this peak even exceeds the signal during combustion. This study analyzes the ion current signal during NVO and its potential to be used for future combustion control concepts. The ion current signal shows potential to stabilize HCCI combustion at high loads. However, the prediction of late combustion cycles is still challenging.

Model Based Control Method for Diesel Engine Combustion

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

In-Cycle Closed-Loop Combustion Control for Pilot Misfire Compensation

<div class="section abstract"><div class="htmlview paragraph">Pilot injections are normally used for the reduction of diesel engine emissions and combustion noise. Nonetheless, with a penalty on the indicated thermal efficiency. The cost is reduced by the minimization of the pilot mass, which on its counterpart increases the risk of pilot misfire. Pilot misfire can have a higher penalty on the indicated efficiency if it is not compensated adequately. This paper investigates how in-cycle closed-loop combustion control techniques can reduce the effects of pilot misfire events.</div><div class="htmlview paragraph">By closed-loop combustion control, pilot misfire can be detected and counteracted in-cycle. Two injection strategies are investigated. The first is the control of the main injection, the second includes an additional second pilot injection. Based on the in-cycle misfire diagnose, two architectures are investigated. The first uses a cycle-to-cycle controller to set the main injection under each scenario. The second is a fully in-cycle controller with feedback from predictive models. All the algorithms were tested experimentally in a Scania D13 engine.</div><div class="htmlview paragraph">The results confirmed that in-cycle closed-loop combustion control can effectively reduce the effects of pilot misfire. An error of +1.5CAD on the main SOC and −0.5bar IMEP on the engine load was reduced to 0±0.6CAD and 0±0.4bar IMEP using the cycle-to-cycle architecture. The predictive in-cycle control can further reduce the error of the main SOC to 0±0.4CAD and 0±0.2bar IMEP for the engine load. These two approaches had one degree of freedom, and therefore only one of the combustion timing parameters (SOC or CA50) was regulated successfully. With the additional degree of freedom of the second pilot injection, the misfire effects were not only reduced, but also fully counteracted.</div><div class="htmlview paragraph">The methods are limited by the time window where pilot misfire observability and controllability overlap. This is set by the injections’ separations and the respective ignition delays. The second pilot injection can be further improved by the regulation of its injection timing and duration. The results can enhance the nominal set-up optimization by including the in-cycle controllability and regulation performance in the constraints.</div></div>

Predictive In-Cycle Closed-Loop Combustion Control with Pilot-Main Injections

This paper studies the use of predictive in-cycle close-loop combustion control to reduce the stochastic cyclic variations of diesel combustion. The combustion metrics that fully define the pressure trace with a pilot-main injection i.e. pilot and main start of combustion, burned pilot mass, and engine load are used as the set-point reference. These metrics are in-cycle predicted by calibrated models as functions of the current cylinder state, estimated by in-cylinder pressure measurements. The proposed approach uses four individual controllers for the set-point error minimization, which respectively regulate the injection’s timing and duration of the pilot-main injection. The controllers are implemented in a FPGA and tested in a Scania D13 engine. The steady-state error reduction, disturbance rejection and transient response are discussed. The results confirm the error reduction in both, cycle-to-cycle and cylinder-to-cylinder variations. The error dispersion, measured by the 95% confidence interval, was reduced between 25% and 75% for all the controlled parameters. By on-line adaptation, the controllers are robust against model uncertainties and fuel types.

Cylinder Pressure Based Method for In-Cycle Pilot Misfire Detection

<div class="section abstract"><div class="htmlview paragraph">For the reduction of emissions and combustion noise in an internal combustion diesel engine, multiple injections are normally used. A pilot injection reduces the ignition delay of the main injection and hence the combustion noise. However, normal variations of the operating conditions, component tolerances, and aging may result in the lack of combustion i.e. pilot misfire. The result is a lower indicated thermal efficiency, higher emissions, and louder combustion noise. Closed-loop combustion control techniques aim to monitor in real-time these variations and act accordingly to counteract their effect. To ensure the in-cycle controllability of the main injection, the misfire diagnosis must be performed before the start of the main injection. This paper focuses on the development and evaluation of in-cycle algorithms for the pilot misfire detection.</div><div class="htmlview paragraph">Based on in-cylinder pressure measurements, different approaches to the design of the detectors are compared. For non-adaptive methods, a constant threshold, direct misfire probability, and posterior misfire probability detectors are investigated. For adaptive methods, an adaptive threshold update is suggested, an adaptation of the predictive stochastic models and a sensor fusion of them is proposed to increase the detection performance.</div><div class="htmlview paragraph">A Scania D13 engine is used to perform the experiments under different operating conditions. The effectiveness of the algorithms is tested for different engine speeds, rail pressures, injection durations, starts of injection, EGR levels, and fuels. The results show that the observability of in-cycle pilot misfire depends on the operating conditions, and its detection can be performed successfully before the start of the main injection. With a maximum in-cycle pilot misfire observability of 98.5%, a maximum successful detection ratio of about 96% can be reached with the proposed in-cycle pilot misfire detectors. The algorithms are therefore suitable for in-cycle closed-loop combustion control feedback. By including cycle-to-cycle adaptation, the detection performance and robustness are improved significantly. The limitations are directly related to the signal-to-noise ratio of each operating condition.</div></div>

CLOSED-LOOP COMBUSTION CONTROL IN PPCI ENGINE

Low-temperature combustion (LTC) is a method which describes extremely low NOx and soot concentrations in an internal combustion engine. Absolutely, the element that mainly affects to mixture fuel-air premixed and combustion in PPCI engines is chemical kinetics. In PPCI combustion, the pressure, and temperature of the mixture inside the cylinder influence the duration of combustion. This study describes closed-loop combustion control in PPCI engines. Initially, control variables are necessary for this method. Secondly, it considers the combustion feedback sensors and control drives burn. The final section presents the PPCI closed-loop combustion control method.

Comparison between Pressure- and Ion-Current-Based Closed-Loop Combustion Control Performance

Comparison between Pressure- and Ion-Current-Based Closed-Loop Combustion Control Performance