Unscented Kalman Filter for Estimation of Manifold Absolute Pressure in Engines

R. Shanmughasundaram; P. Supriya; T. N. Padmanabhan Nambiar

doi:10.15866/iremos.v16i5.23641

Unscented Kalman Filter for Estimation of Manifold Absolute Pressure in Engines

R. Shanmughasundaram^(1*), P. Supriya⁽²⁾, T. N. Padmanabhan Nambiar⁽³⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/iremos.v16i5.23641

Abstract

In a global scenario, the exhaust emissions of automobiles need to be regulated. These emission regulations are met by maintaining the air-fuel ratio resulting in improved automobile performance like better engine torque and fuel economy. The air-fuel ratio of Spark Ignition (SI) engines needs to be maintained at a stoichiometric value of 14.7:1. To maintain this ratio, the estimation of the in-cylinder mass airflow rate is highly significant. One of the important parameters that influence the mass airflow rate is Manifold Absolute Pressure (MAP). The conventional methods that make use of MAP sensors have oscillations at engine firing frequency and poor dynamic response. This research paper uses an Unscented Kalman Filter (UKF) for the estimation of MAP in SI engines. The complete system consisting of the engine model and UKF algorithm has been simulated in Matlab/Simulink. With Root Mean Square Error (RMSE) as the performance metric, the accuracy in the estimation of MAP with UKF is higher than the Extended Kalman Filter (EKF). Hence, the UKF algorithm works out to be a better estimator for the efficient fuel injection in an engine management system.
Copyright © 2023 Praise Worthy Prize - All rights reserved.

Keywords

Estimation; Extended Kalman Filter; Manifold Absolute Pressure; Unscented Kalman Filter

Full Text:

PDF

References

Ashok, B.S., Denis Ashok and Ramesh Kumar, C, A Review on Control System Architecture of an SI Engine Management System, Annual Reviews in Control, Vol. 41:94-118, 2016.
https://doi.org/10.1016/j.arcontrol.2016.04.005

Gutiérrez González, E., Alvarez Flórez, J., Arab, S, Development of the management strategies of the ECU for an Internal Combustion Engine, Mechanical Systems and Signal Processing, Vol.22(Issue 6):1356-1373, 2008.
https://doi.org/10.1016/j.ymssp.2007.11.030

Hendricks, E., Poulsen, J., Olsenm M.B., Jensen P.B., Fons M., Jepsen C, Alternative observers for SI engine air/fuel ratio control, IEEE Conference on Decision and Control, pp. 2806-2811, Kobe, Japan, 1996.
https://doi.org/10.1109/CDC.1996.573541

David Powell, J., Fekete N.P. and Chang C.F, Observer-Based Air-Fuel Ratio Control, IEEE Control Systems, Vol. 18(Issue 5): 72-83, 1998.
https://doi.org/10.1109/37.722254

Meng, L., Luo, J., Yang, X. and Zeng, C, Intake air mass observer design based on extended Kalman filter for air-fuel ratio control on SI engine, Energies, Vol. 12(Issue 18):1-12, 2019.
https://doi.org/10.3390/en12183444

Choi, S.B. and Hedrick, J.K., An observer-based controller design method for improving air/fuel characteristics of spark ignition engines, IEEE Transactions on Control Systems Technology, Vol. 6(Issue 3):325-334, 1998.
https://doi.org/10.1109/87.668034

Kolewe, B., Haghani, A., Beckmann, R., Noack, R., Jeinsch T, Data-driven estimation of air mass using Gaussian mixture regression, IEEE 23rd International Symposium on Industrial Electronics (ISIE), pp. 2433-2438, Istanbul, Turkey, 2014.
https://doi.org/10.1109/ISIE.2014.6865001

Wang, Z., Zhu, Q., Prucka, R., Prucka, M. and Dourra, H, Observer-based cylinder air charge estimation for spark-ignition engines, Journal of Engineering for Gas Turbines and Power, Vol. 139(Issue 10):102806, 2017.
https://doi.org/10.1115/1.4036360

Wang, C., Liang, M, Li, C.N, Adaptive Fuzzy Sliding Mode Observer for Cylinder Mass Flow Estimation in SI Engines, IEEE Access, Vol. 6: 29558-29566, 2018.
https://doi.org/10.1109/ACCESS.2018.2840519

Amir Khameneian, Xin Wang, Paul Dice, Jeffrey D. Naber, Mahdi Shahbakhti, Chad Archer, Peter Moilanen, Chris Glugla, Garlan Huberts, A real-time control-oriented discrete nonlinear model development for in-cylinder air charge, residual gas and temperature prediction of a Gasoline Direct Injection engine using cylinder, intake and exhaust pressures, Control Engineering Practice, Vol. 119: 2022.
https://doi.org/10.1016/j.conengprac.2021.104978

Yongsoon Yoon, Observer-based Purge Estimation for Robust Air Fuel Ratio Control of Internal Combustion Engines, IFAC-Papers On-Line, Vol. 55 (Issue 24): 27-32 2022.
https://doi.org/10.1016/j.ifacol.2022.10.257

Julier, S.J., Uhlmann J.K, Unscented filtering and nonlinear estimation, Proceedings of the IEEE, Vol. 92(Issue 3): pp. 401-422, 2004.
https://doi.org/10.1109/JPROC.2003.823141

Wan, E.A., Van Der Merwe, R, The unscented Kalman filter for nonlinear estimation, IEEE Adaptive Systems for Signal Processing, Communications and Control Symposium, pp.153-158, Lake Louise, Canada, 2000.
https://doi.org/10.1109/ASSPCC.2000.882463

Kwangmin Kim, Jinsung Kim, Oheun Kwon, Se-Kyu Oh, Yong-Wha Kim, Dongjun Lee, Optimal Estimation of Gasoline LP-EGR via Unscented Kalman Filtering with Mixed Physics-based/Data-driven Components Modeling, IFAC-Papers On-Line, Vol. 53 (Issue 2):144-151, 2020.
https://doi.org/10.1016/j.ifacol.2020.12.112

Noori, O., Mustafa, M., Compressed Extended Kalman Filter for Sensorless Control of Asynchronous Motor, (2020) International Journal on Energy Conversion (IRECON), 8 (6), pp. 200-211.
https://doi.org/10.15866/irecon.v8i6.19202

Hendricks, E., Vesterholm, T, The analysis of mean value SI engine models, SAE Technical Paper, 920682, 1992.
https://doi.org/10.4271/920682

Valencia, G., Duarte Forero, J., Acevedo, C., A Semi-Physical Modeling of the Combustion Chamber and Inlet Manifold of a 2 MW Natural Gas Generation Engine, (2020) International Review on Modelling and Simulations (IREMOS), 13 (1), pp. 26-32.
https://doi.org/10.15866/iremos.v13i1.18748

Thrun, S., Burgard, W., Fox, D., Probabilistic Robotics, (MIT Press, 2005).

Krishnakumar, A. and Shanmughasundaram, R., Simplified SOC estimation by EKF in LI-ION cell, Journal of Advanced Research in Dynamical and Control Systems, Vol. 3: 616-622, 2018.

Shruthi, S and Supriya, P, Modified State Variable Approach using Kalman Filter for Battery Management of Electric Vehicle, International Journal of Control Theory and Applications, 2016.

Sudheesh, P. and Jayakumar, M, Nonlinear tracking using unscented Kalman filter, Advances in Signal Processing and Intelligent Recognition Systems, pp. 38-46, 2018.
https://doi.org/10.1007/978-3-319-67934-1_4

Abhirami and P. Sudheesh, Detection and Tracking of Anxiety Related Diseases for Autism Spectrum Disorder Using ECG, International Conference on Communication and Electronics Systems (ICCES), pp. 1044-1052, Coimbatore, India, 2019.
https://doi.org/10.1109/ICCES45898.2019.9002160

Hammia, S., Hatim, A., Bouaaddi, A., Najoui, M., Jakjoud, F., Ez-ziymi, S., Efficient EKF-SLAM's Jacobian Matrices Hardware Architecture and its FPGA Implementation, (2021) International Review of Electrical Engineering (IREE), 16 (5), pp. 484-496.
https://doi.org/10.15866/iree.v16i5.20149

Bidane, I., Ailane, A., Khamlich, S., Enhanced Control of Overhead Crane System Using First-Order Sliding Mode Control and Extended Kalman Filter Observer, (2023) International Review of Automatic Control (IREACO), 16 (3), pp. 125-133.
https://doi.org/10.15866/ireaco.v16i3.23438

Rached, B., Elharoussi, M., Abdelmounim, E., DSP in the Loop Implementation of Sliding Mode and Super Twisting Sliding Mode Controllers Combined with an Extended Kalman Observer for Wind Energy System Involving a DFIG, (2020) International Journal on Energy Conversion (IRECON), 8 (1), pp. 26-37.
https://doi.org/10.15866/irecon.v8i1.18432

Darabseh, T., Nonlinear State Dependent Riccati Equation Controller for 3-DOF Airfoil with Cubic Structural Nonlinearity, (2022) International Review of Aerospace Engineering (IREASE), 15 (2), pp. 97-111.
https://doi.org/10.15866/irease.v15i2.21426

Habib, T., Abouhogail, R., Efficient Simultaneous Spacecraft Attitude and Orbit Estimation via Neural Networks, (2021) International Review of Aerospace Engineering (IREASE), 14 (6), pp. 346-353.
https://doi.org/10.15866/irease.v14i6.20312

Habib, T., Replacement of In-Orbit Modern Spacecraft Attitude Determination and Estimation Algorithms with Neural Networks, (2021) International Review of Aerospace Engineering (IREASE), 14 (3), pp. 166-172.
https://doi.org/10.15866/irease.v14i3.19687

Habib, T., Abouhogail, R., Replacement of In-Orbit Spacecraft Attitude Determination Algorithms with Adaptive Neuro-Fuzzy Inference System via Subtractive Clustering, (2021) International Review of Aerospace Engineering (IREASE), 14 (4), pp. 220-227.
https://doi.org/10.15866/irease.v14i4.20020

Refbacks

There are currently no refbacks.

Username
Password
Remember me