MPC-based power management strategy for a series hybrid electric tracked bulldozer

In this brief, a model predictive control (MPC) is developed for the first time to solve the optimal energy management problem in tracked bulldozers equipped with advanced series hybrid powertrains. Hybrid bulldozers use two distinct power sources for propulsion, and their complex powertrain architecture requires the coordination of all subsystems to achieve target performances in terms of fuel economy, exhaust emissions. This method is applied to a series hybrid electric vehicle, using a linearized model in state space formulation and a linear MPC algorithm, based on Quadratic Programming (QP), to find a feasible suboptimal solution. The MPC solution is then compared with the dynamic programming algorithm, which requires the entire driving profile to be known priori, guarantees the optimality and is used here as the benchmark solution. The effect of the parameters of the MPC (length of prediction horizon) is also investigated. The results from comparing the MPC solution and the rule-based control strategy indicate that there is an approximately 5.2%improvement in fuel economy.