Integrated methodology for state and parameter estimation of spark-ignition engines

Abstract

To develop an effective control and monitoring scheme for automotive engines, a precise knowledge of the parameters and unmeasurable states of the nonlinear model capturing the overall dynamics of engines is of utmost importance. For a new vehicle out of the assembly line, the nonlinear model has constant parameters. However, in the long run, due to regular wear-and-tear, and for other unpredictable disturbances, they may change. The main challenges are how to obtain the information of parameters and states under the influence of process noise and measurement noise. To address these challenges, we present a new integrated state and parameter estimation algorithm in this paper for spark ignition (SI) engines based on the constrained unscented Kalman filter and the improved recursive least square technique. The system under consideration is a highly nonlinear mean value SI engine model consisting of the throttle, intake manifold, engine speed dynamics, and fuel system. The performance of the proposed algorithm in terms of root-mean-square-error and robustness with regards to initial conditions and random noises is analysed through exhaustive simulation scenarios considering constant, and time-varying parameters. In addition, the performance of other state-of-the-art estimation algorithms is also compared with that of the developed integrated algorithm.

Keywords:

• Spark-ignition engine
• recursive least-squares
• unscented Kalman filter
• time-varying parameter estimation
• nonlinear mean-value-engine model

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Science and Engineering Research Board (SERB), Government of India, India under the grant agreement no. MTR/2019/001582.

Notes on contributors

Vyoma Singh

Vyoma Singh is a Ph.D. student at the School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, India. Her current research work is to investigate state and parameter estimation algorithms for spark-ignition engines, in collaboration with Robert Bosch Limited, Bangalore, India. She obtained M.Tech in Signal Processing and Control from the National Institute of Technology Hamirpur, India in 2015 and B.Tech in Electrical Engineering from the National Institute of Technology Srinagar, India in 2013. Her areas of interest are system identification, state estimation, Kalman filters, system modeling, and control.

Birupaksha Pal

Birupaksha Pal is a researcher at Robert Bosch research and technology center, Bangalore. His research interest and work focuses on numerical methods for real time embedded applications, system identification, digital twins, parameter estimation/optimization methods and hybrid models. He comes from a background of mathematics and prior to joining Bosch he completed his PhD in computational fluid dynamics from the department of computational and data science at Indian Institute of Science, Bangalore and holds a masters in Mathematics from Indian Institute of Technology, Bombay.

Tushar Jain

Tushar Jain received the degree of Doctor in Control, Identification and Diagnostic from Université de Lorraine, Nancy, France in 2012. He previously received the degree of M.Tech. in System modelling and control from Indian Institute of Technology (IIT) Roorkee in 2009. From 2013 to 2014 and 2014 to 2015, he was a Post-doc researcher and Academy of Finland researcher, respectively in the Research Group of Process Control at Aalto University, Finland. Since 2015, he is with the School of Computing and Electrical Engineering, IIT Mandi. During these last years, his research activities have been focused on various topics of systems theory and its application to problems in control engineering with special interests in fault-tolerant control, fault diagnosis and optimal control problems. He has received the best paper award thrice for his research work. He has authored a book entitled Active Fault-Tolerant Control Systems: A Behavioral System Theoretic Perspective (Springer, 2018). He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE).