Data-driven algorithms for engine friction estimation

Abstract

Errors in the estimation of friction torque in modern spark ignition automotive engines necessitate the development of real-time algorithms for adaptation of the friction torque. Friction torque in the engine control unit is presented as a look-up table with two input variables (the engine speed and indicated engine torque). The algorithms proposed in this paper estimate the engine friction torque via the crankshaft speed fluctuations at the fuel cut-off state and at idle. A computationally efficient filtering algorithm for reconstruction of the first harmonic of a periodic signal is used to recover an amplitude which corresponds to engine events from the noise-contaminated engine speed measurements at the fuel cut-off state. The values of the friction torque at the nodes of the look-up table are updated, when new measured data of the friction torque are available. New data-driven algorithms which are based on a stepwise regression method are developed for adaptation of look-up tables. The algorithms are verified by using a spark ignition six-cylinder prototype engine.