Real-time Markov chain driver model for tracked vehicles

The design of an energy management strategy for a hybrid electric vehicle typically requires an estimate of requested power from the driver. If the driving cycle is not known a priori, stochastic method such as a Markov chain driver model (MCDM) must be employed. For tracked vehicles, steering power, which is related to the vehicle angular velocity, is a significant component of the driver demand. In this paper, a three-dimensional MCDM incorporating angular velocity for a tracked vehicle is proposed. Based on the nearest-neighborhood method (NNM), an online transition probability matrix (TPM) updating algorithm is implemented for the three-dimensional MCDM. Simulation results show that the TPM is able to update online when the driving cycle is available. Moreover, the older and recent observations can be weighted appropriately by adjusting a forgetting factor.