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

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.

Automated summary

The study focuses on developing a new integrated state and parameter estimation algorithm for solving control and monitoring issues of automotive engines. The algorithm is based on the constrained unscented Kalman filter and the improved recursive least square technique specifically for spark ignition engines.