Gray-box modeling for performance control of an HCCI engine with blended fuels

High fidelity models that balance accuracy and computation load are essential for real-time model-based control of homogeneous charge compression ignition (HCCI) engines. Gray-box modeling offers an effective technique to obtain desirable HCCI control models. In this paper, a physical HCCI engine model is combined with two feed-forward artificial neural network models to form a serial architecture gray-box model. The resulting model can predict three major HCCI engine control outputs, including combustion phasing, indicated mean effective pressure (IMEP), and exhaust gas temperature (Texh). The gray-box model is trained and validated with the steady-state and transient experimental data for a large range of HCCI operating conditions. The results indicate that the gray-box model significantly improves the predictions from the physical model. For 234 HCCI conditions tested, the gray-box model predicts combustion phasing, IMEP, and Texh with an average error of less than 1 crank angle degree, 0.2 bar, and 6 °C, respectively. The gray-box model is computationally efficient and it can be used for real-time control application of HCCI engines. Copyright © 2014 by ASME

Grey-box modeling for hcci engine control

High fidelity models that balance accuracy and computation load are essential for real-time model-based control of Homogeneous Charge Compression Ignition (HCCI) engines. Grey-box modeling offers an effective technique to obtain desirable HCCI control models. In this paper, a physical HCCI engine model is combined with two feed-forward artificial neural networks models to form a serial architecture grey-box model. The resulting model can predict three major HCCI engine control outputs including combustion phasing, Indicated Mean Effective Pressure (IMEP), and exhaust gas temperature (Texh). The grey-box model is trained and validated with the steady-state and transient experimental data for a large range of HCCI operating conditions. The results indicate the grey-box model significantly improves the predictions from the physical model. For 234 HCCI conditions tested, the grey-box model predicts combustion phasing, IMEP, and Texh with an average error less than 1 crank angle degree, 0.2 bar, and 6 °C respectively. The grey-box model is computationally efficient and it can be used for real-time control application of HCCI engines. © 2013 by ASME

Integrated hcci engine control based on a performance index

Integrated control of HCCI combustion phasing, load, and exhaust aftertreatment system is essential for realizing high efficiency HCCI engines, while maintaining low HC and CO emissions. This paper introduces a new approach for integrated HCCI engine control by defining a novel performance index to characterize different HCCI operating regions. The experimental data from a single cylinder engine at 214 operating conditions is used to determine the performance index for a blended fuel HCCI engine. The new performance index is then used to design an optimum reference trajectory for a multi-input multi-output HCCI controller. The optimum trajectory is designed for control of combustion phasing and IMEP, while meeting catalyst light-off requirements for the exhaust aftertreatment system. The designed controller is tested on a previously validated physical HCCI engine model. The simulation results illustrate the successful application of the new approach for controller design of HCCI engines. © 2013 by ASME

Cycle-to-cycle modeling and sliding mode control of blended-fuel HCCI engine

Fast and robust control of combustion phasing is an important challenge for real-time model-based control of Homogenous Charge Compression Ignition (HCCI). In this paper a new discrete Control Oriented Model (COM) for predicting HCCI combustion phasing on a cycle-to-cycle basis is outlined and validated against experimental data from a single cylinder Ricardo engine. The COM has sufficient accuracy for real-time HCCI control and can be implemented in real-time.A Discrete Sliding Mode Controller (DSMC) coupled with a Kalman filter is designed to control combustion phasing by adjusting the ratio of two Primary Reference Fuels (PRFs). The results indicate the DSMC maintains the stability of the engine operation in a wide range of loads and speeds. The DSMC is compared with an empirical Proportional Integral (PI) controller. The results show the SMC outperforms a PI controller particularly in rejecting disturbances while maintaining HCCI combustion phasing in its desired range. © 2013 Elsevier Ltd

Grey-box modeling of HCCI engines

Homogenous Charge Compression Ignition (HCCI) holds promise to increase indicated thermal efficiency and reduce Nitrogen Oxides (NOx) and Particulate Matter (PM) emissions from internal combustion engines. Lack of a direct means to initiate the combustion and high levels of Total Hydrocarbon (THC) and Carbon Monoxide (CO) emissions are major drawbacks associated with HCCI engines. Control of combustion phasing for optimum indicated thermal efficiency and minimizing emissions are vital for putting HCCI engines into practice. One major challenge is to develop accurate models for understanding engine performance, as those models can run real-time for HCCI control. This paper develops the first computationally efficient grey-box model for predicting major HCCI engine variables. The grey-box model consists of a combination of physical models and three feed-forward artificial neural networks models to estimate six major HCCI variables including combustion phasing, load, exhaust gas temperature, THC, CO, and NOx emissions. The grey-box model is experimentally validated over a large range of HCCI engines operation including 309 steady state and transient test conditions. The validation results show that the grey-box model is able to predict the HCCI engine outputs with average relative errors less than 10%. Performance of the grey-box methodology is tested for two different HCCI engines and the verification results show that the developed six-output grey-box model can be successfully used for performance modeling of different HCCI engine applications. © 2014 Elsevier Ltd. All rights reserved

Optimal exergy-based control of internal combustion engines

© 2016 Elsevier Ltd Exergy or availability is defined as the maximum useful work during a process. This metric has been used to analyze and understand loss mechanisms of Internal Combustion Engines (ICEs). In this paper, an optimal control method based on exergy is introduced for transient and steady state operation of ICEs. First, an exergy model is developed for a single cylinder Ricardo engine. The ICE exergy model is based on the Second Law of Thermodynamics (SLT) and characterizes irreversibilities. Such quantifications are not identified in the First Law of Thermodynamics (FLT) analysis. For steady-state operation of the ICE, a set of 175 different operating conditions is used to construct the SLT efficiency maps. Two different SLT efficiency maps are generated depending on the applications whether work, or Combined Power and Exhaust Exergy (CPEX) is the desired output. To include transient ICE operation, a model to predict exergy loss/destruction during engine transients is developed. The sources of exergy destruction/loss are identified for a Homogeneous Charge Compression Ignition (HCCI) engine. Based on the engine operating conditions (i.e., steady-state or transient) SLT efficiency contour maps or predicted exergy losses are determined at every given engine load. An optimization algorithm is proposed to find the optimum combustion phasing to maximize the SLT efficiency. Application of the optimization algorithm is illustrated for combustion phasing control. The results show that using the exergy-based optimal control strategy leads to an average of 6.7% fuel saving and 8.3% exergy saving compared to commonly used FLT based combustion control in which a fixed combustion phasing (e.g., 8°aTD) is used