Control-Oriented Model-Based Burn Duration and Ignition Timing Prediction With Recursive-Least-Square Adaptation for Closed-Loop Combustion Phasing Control of a Spark Ignition Engine

2019 ◽  
Author(s):  
Xin Wang ◽  
Amir Khameneian ◽  
Paul Dice ◽  
Bo Chen ◽  
Mahdi Shahbakhti ◽  
...  
Keyword(s):  
Operating Conditions ◽  
Least Square ◽  
Prediction Algorithm ◽  
Combustion Model ◽  
Model Errors ◽  
Ignition Timing ◽  
Recursive Least Square ◽  
Adaptation Algorithm ◽  
Model Based ◽  
Si Engines

Abstract In homogeneous spark-ignition (SI) engines, ignition timing is used to control the combustion phasing (crank angle of fifty percent of fuel burned, CA50), which affects fuel economy, engine torque output, and emissions. This paper presents a model-based adaptive ignition timing prediction strategy using a control-oriented dynamic combustion model for real-time closed-loop combustion phasing control. The combustion model predicts the burn duration from ignition timing to CA50 (ΔθIGN-CA50) at Intake Valve Closing (IVC) for the upcoming cycle based on current engine operating conditions, including variable valve timing, predicted ignition timing, air-fuel ratio, engine speed, and engine load. To maintain the accuracy of combustion model and ignition timing prediction during the engine lifetime, a Recursive-Least-Square (RLS) with Variable Forgetting Factor (VFF) based adaptation algorithm is developed to handle both short term (operating-point-dependent) and long term (engine aging) model errors. Due to short term model errors and stochastic characteristics of cycle-to-cycle combustion variations, large model errors may occur during severe transient operating conditions (tip-in/tip-out), which can result in wrong adjustments and excessive adaptations. Since on-road SI engines are always operating in transient conditions, the ‘Heavy Transient Detection’ algorithm is developed to avoid fault adaptation and assist the adaptation algorithm to be stable. On-road vehicle testing data is used to evaluate the performance of the entire model-based adaptive burn duration and ignition timing prediction algorithm. With only 64 calibration points, a mean ignition timing prediction error of 0.2 Crank Angle Degree (CAD) and average iteration number of 2 shows the capability of adaptive ignition timing prediction, a significant reduction of calibration efforts, and potential of real-time application of the developed adaptive ignition timing prediction algorithm.

Model-Based Combustion Duration and Ignition Timing Prediction for Combustion Phasing Control of a Spark-Ignition Engine Using In-Cylinder Pressure Sensors

2019 ◽  
Author(s):  
Xin Wang ◽  
Amir Khameneian ◽  
Paul Dice ◽  
Bo Chen ◽  
Mahdi Shahbakhti ◽  
...  
Keyword(s):  
Pressure Sensors ◽  
Spark Ignition ◽  
Operating Conditions ◽  
Spark Ignition Engine ◽  
Combustion Model ◽  
Ignition Timing ◽  
Model Based ◽  
Crank Angle ◽  
Si Engines ◽  
Phasing Control

Abstract Combustion phasing, which can be defined as the crank angle of fifty percent mass fraction burned (CA50), is one of the most important parameters affecting engine efficiency, torque output, and emissions. In homogeneous spark-ignition (SI) engines, ignition timing control algorithms are typically map-based with several multipliers, which requires significant calibration efforts. This work presents a framework of model-based ignition timing prediction using a computationally efficient control-oriented combustion model for the purpose of real-time combustion phasing control. Burn duration from ignition timing to CA50 (ΔθIGN-CA50) on an individual cylinder cycle-by-cycle basis is predicted by the combustion model developed in this work. The model is based on the physics of turbulent flame propagation in SI engines and contains the most important control parameters, including ignition timing, variable valve timing, air-fuel ratio, and engine load mostly affected by combination of the throttle opening position and the previous three parameters. With 64 test points used for model calibration, the developed combustion model is shown to cover wide engine operating conditions, thereby significantly reducing the calibration effort. A Root Mean Square Error (RMSE) of 1.7 Crank Angle Degrees (CAD) and correlation coefficient (R2) of 0.95 illustrates the accuracy of the calibrated model. On-road vehicle testing data is used to evaluate the performance of the developed model-based burn duration and ignition timing algorithm. When comparing the model predicted burn duration and ignition timing with experimental data, 83% of the prediction error falls within ±3 CAD.

scholarly journals Development of A Modified Extended Model-Based Fault Detection And Diagnosis Approach

2021 ◽  
Author(s):  
Alireza Alikhani ◽  
Ghasem Sharifi
Keyword(s):  
Fault Detection ◽  
Adaptive Estimation ◽  
Response Speed ◽  
Previous Method ◽  
Fault Detection And Diagnosis ◽  
Least Square ◽  
Extended Model ◽  
Multiple Model ◽  
Recursive Least Square ◽  
Model Based

Abstract A supervisory system for space missions is critical due to the high risk of missions, the costs, and the impossibility of adding redundancy. The model-based fault detection approaches are of interest due to their highly responsive speed, robustness against disturbances and uncertainties, and accuracy. Conventional model-based methods have some drawbacks such as feasibility and applicability. In this paper, a modified extended multiple models adaptive estimation (MEMMAE) method is developed which keep both the advantages of the previous model-based methods and take into account some limitations of that. This approach can be performed on various systems to detect and diagnose faults, with appropriate response speed and resistance to uncertainty and disturbances. By combining the recursive least-square algorithm with the extended multiple model adaptive estimation (EMMAE) method, the limitations of this method including simultaneous fault detection, diagnostics of failure cause, and high processing volume are eliminated. The method is implemented on a spacecraft as a case study using the MATLAB/SIMULINK software and demonstrates that the responsive speed and accuracy of the proposed method is significantly much more effective and accurate than the previous method.

Control-Oriented Model Development and Experimental Validation for a Modern Diesel Engine

2020 ◽  
Author(s):  
Kuo Yang ◽  
Pingen Chen
Keyword(s):  
Diesel Engine ◽  
Diesel Engines ◽  
Model Development ◽  
Engine Performance ◽  
Operating Conditions ◽  
Least Square ◽  
Injection Timing ◽  
Model Based ◽  
Wide Range ◽  
Mean Square Errors

Abstract Modern Diesel engines have become highly complex multi-input multi-output systems. Controls of modern Diesel engines to meet various requirements such as high fuel efficiency and low NOx and particulate matter (PM) emissions, remain a great challenge for automotive control community. While model-based controls have demonstrated significant potentials in achieving high Diesel engine performance. Complete and high-fidelity control-oriented Diesel engine models are much needed as the foundations of model-based control system development. In this study, a semi-physical, mean-value control-oriented model of a turbocharged Diesel engine equipped with high-pressure exhaust gas recirculation (EGR) and variable geometry turbocharger (VGT) is developed and experimentally validated. The static calibration of Diesel engine model is achieved with the least-square optimization methodology using the experimental test data from a physical Diesel engine platform. The normalized root mean square errors (NRMSEs) of the calibration results are in the range of 0.1095 to 0.2582. The cross-validation results demonstrated that the model was capable of accurately capturing the engine torque output and NOx emissions with the control inputs of EGR, VGT and Start of Injection timing (SOI) in wide-range operating conditions.

Vehicle Trajectory Prediction Based on Road Recognition

2014 ◽  
Vol 599-601 ◽  
pp. 760-766
Author(s):  
Qing Bo Shao ◽  
Hsin Guan ◽  
Xin Jia
Keyword(s):  
Autonomous Vehicle ◽  
Recognition Algorithm ◽  
Least Square ◽  
Prediction Algorithm ◽  
Recursive Least Square ◽  
Trajectory Prediction ◽  
Dynamic Correction ◽  
The Road ◽  
Road Friction ◽  
Vehicle Trajectory

Predicting vehicle trajectory accurately is a crucial task for an autonomous vehicle. It is also necessary for many Advanced Driver Assistance System to predict trajectory of the ego-vehicle’s. In recent years, some vehicles trajectory prediction algorithm is mainly based on a simple Motion Model. This paper puts forward a method which combines road recognition and the hypothesis of steady preview and dynamic correction for trajectory prediction. In the road recognition algorithm, both methods of Kalman Filter (KF) and Recursive Least-Square (RLS) work well to estimate the road slope and road friction coefficient.

Electrochemical Model-Based Adaptive Estimation of Li-Ion Battery State of Charge

2015 ◽  
Author(s):  
N. Lotfi ◽  
R. G. Landers ◽  
J. Li ◽  
J. Park
Keyword(s):  
Adaptive Estimation ◽  
Lyapunov Theory ◽  
Operating Conditions ◽  
Particle Model ◽  
Li Ion Battery ◽  
Adaptation Algorithm ◽  
Model Based ◽  
Online Implementation ◽  
Li Ion ◽  
Electrochemical Model

Electrochemical model-based estimation techniques have attracted increasing attention in the past decade due to their inherent insight about the internal battery operating conditions and limits while being able to monitor important li-ion battery states. The applicability of these methods is, however, limited due to the implementation complexity of their underlying models. In order to facilitate online implementation while maintaining the physical insight, a reduced-order electrochemical model is used in this work. This model, which is based on the commonly-used single particle model, is further improved by incorporating the electrolyte-phase potential. Furthermore, an output-injection observer, suitable for online implementation, is proposed to estimate SOC. The observer convergence is proved analytically using Lyapunov theory. Although the proposed observer shows great performance at low C rates, its accuracy deteriorates at high C-rates. To overcome this issue and achieve accurate SOC estimates for such charge/discharge rates, an adaptation algorithm is augmented to the observer. The adaptation algorithm, which can be implemented online, is used to compensate for model uncertainties, especially at higher C rates. Finally, simulation results based on a full-order electrochemical model are used to validate the observer performance and effectiveness.

Online Spark Timing Optimization With Complex High-Fidelity Combustion Phasing, Knock, and Coefficient of Variation of IMEP Models

2021 ◽  
Vol 143 (6) ◽  
Author(s):  
Qilun Zhu ◽  
Robert Prucka ◽  
Shu Wang ◽  
Michael Prucka
Keyword(s):  
Coefficient Of Variation ◽  
Least Square ◽  
Recursive Least Square ◽  
Two Phase ◽  
Relaxation Methods ◽  
Model Inversion ◽  
Combustion Models ◽  
Indicated Mean Effective Pressure ◽  
Spark Timing ◽  
Si Engines

Abstract The combustion phasing of spark ignition (SI) engines is traditionally regulated with map-based spark timing (SPKT) control. The calibration of these maps is time-consuming for SI engines with a high number of control actuators. This paper proposes three online SPKT optimization algorithms that can utilize control-oriented semiphysics-based combustion models making the SPKT control algorithm more adaptive to different engine designs. These three SPKT optimizers do not require model inversion and derivative information. These methods also preserve the dependence between combustion phasing, knock, and coefficient of variation (COV) of indicated mean effective pressure (IMEP) models to avoid evaluating combustion models multiple times within one iteration. The two-phase and constraint relaxation methods are derived from direct search optimization theories. The recursive least square (RLS) polynomial fitting method can be considered as a virtual extreme seeking (ES) process that converts the original “black” box nonlinear constrained optimization into the solution of three low-order polynomial equations. Although these three online SPKT optimization approaches have unique properties making them preferable with certain types of combustion models, simulation and test results show that all of them can find the optimal SPKT with less than 10 evaluations of the combustion models. This fact makes it possible to implement the proposed model-based SPKT control strategy in future engine control units (ECUs).

A two-zone reaction-based combustion model for a spark-ignition engine

2019 ◽  
Vol 22 (1) ◽  
pp. 109-124 ◽  
Author(s):  
Ruixue C Li ◽  
Guoming G Zhu ◽  
Yifan Men
Keyword(s):  
Real Time ◽  
Combustion Process ◽  
Operating Conditions ◽  
Spark Ignition Engine ◽  
Combustion Model ◽  
Individual Species ◽  
Cylinder Pressure ◽  
Model Based ◽  
Modeling Approach ◽  
Real Time Simulations

This article presents a control-oriented two-zone reaction-based zero-dimensional model to accurately describe the combustion process of a spark-ignited engine for real-time simulations, and the developed model will be used for model-based control design and validation. A two-zone modeling approach is adopted, where the combustion chamber is divided into the burned (reaction) and unburned (pre-mixed) zones. The mixture thermodynamic properties and individual chemical species in two zones are taken into account in the modeling process. Instead of using the conventional pre-determined Wiebe-based combustion model, a two-step chemical reaction model is utilized to predict the combustion process along with important thermodynamic parameters such as the mass-fraction-burned, in-cylinder pressure, temperatures, and individual species mass changes in both zones. Sensitivities of model parameters are analyzed during the model calibration process. As a result, one set of calibration parameters is used to predict combustion characteristics over all engine operating conditions studied in this article, which is the major advantage of the proposed method. Also, the proposed modeling approach is capable of modeling the combustion process under different air-to-fuel ratios, ignition timings, and exhaust-gas-recirculation rates for real-time simulations. As the by-product of the model, engine knock can also be predicted based on the Arrhenius integral in the unburned zone, which is valuable for model-based knock control. The proposed combustion model is intensively validated using the experimental data with a peak relative prediction error of 6.2% for the in-cylinder pressure.

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