Energy management strategy for hybrid electric tracked vehicle based on stochastic dynamic programming

Energy management strategy is indispensable and should be designed with the constraints from vehicle's drivability and economy for the hybrid electric tracked vehicle with the engine-generator set and the power battery pack simultaneously. An energy management strategy based on stochastic dynamic programming is proposed for a serial hybrid electric tracked vehicle. Based on the driving schedule from the field test data, the probability of power demand is calculated by maximum likelihood method, the power demand for driver is modeled as a Markov chain process which varies as the vehicle speed changes. The engine-generator set, battery and power balance between them is modeled. The fuel consumption and the deviation between final and initial state of charge of battery were adopted as the cost value function of the optimal control model. A policy iteration algorithm method is applied to solve the optimal control problem and the optimal control will be determined by the state feedbacks including the engine speed and the battery state of charge, the vehicle speed and the power request. The control strategy is verified through the feed-forward simulation model and field test. A considerable improvement in fuel economy, compared to the former rule-based control strategy, is observed.