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The paper presents the estimator design procedures for automotive power train systems based on the adaptive Kalman filter. The Kalman filter adaptation is based on a simple and robust algorithm that detects sudden changes of power train variables. The adaptive Kalman filter has been used to estimate the SI engine load torque and air mass flow, and also the tire traction force and road condition. The presented experimental results indicate that proposed estimators are characterized by favorable response speeds and good noise suppression abilities.

The topic of energy management in Hybrid Electric Vehicles (HEVs) has received a great deal of recent attention. Various methods have been proposed to develop algorithms which manage energy flows within HEVs so that to optimally exploit energy storage capability of the battery and reduce fuel consumption while maintaining battery State of Charge. In addition, to the rule-based approaches, systematic optimal control methods based on deterministic and stochastic dynamic programming have been explored for HEV energy management optimization. In this paper, another novel framework based on the application of game theory is proposed, in which the HEV operation is viewed as a non-cooperative game between the driver and the powertrain.