A Real-Time Markov Chain Driver Model for Tracked Vehicles and Its Validation: Its Adaptability via Stochastic Dynamic Programming

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 methods, such as a Markov chain driver model (MCDM), must be employed. For tracked vehicles, the steering power, which is related to vehicle angular velocity, is a significant component of the driver demand. In this paper, a three-dimensional (3-D) MCDM incorporating angular velocity for a tracked vehicle is proposed. Based on the nearest-neighborhood method, an online transition probability matrix (TPM)-updating algorithm is implemented for the 3-D MCDM. Simulation results show that the TPM is able to update online and adapt to the changing driving conditions. Moreover, the adaptability of the online TPM updating algorithm to the change in driving is validated via a stochastic dynamic programming approach for a series hybrid tracked vehicle. Results show that the online updating for the MCDM's TPM is competent for adapting to the changing driving conditions.