Data-driven algorithms for engine friction estimation

Errors in an estimate of friction torque in modern spark ignition automotive engines have a direct impact on a drivability performance of a vehicle and necessitate a 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 (engine speed and indicated engine torque). Algorithms proposed in this paper estimate the engine friction torque via the crankshaft speed fluctuations at the fuel cut off state and at idle. 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 is available. New data-driven algorithms which are based on a step-wise regression method are developed for adaptation of look-up tables. Algorithms are verified by using a spark ignition six cylinder prototype engine