Receding horizon control-based energy management for plug-in hybrid electric buses using a predictive model of terminal SOC constraint in consideration of stochastic vehicle mass

The factor of stochastic vehicle mass can greatly affect the optimality of energy management for plug-in hybrid electric buses. However, current studies usually take the vehicle mass as constant, which is inevitably far away from reality. This paper responds to this problem by investigating a receding horizon control (RHC)-based energy management together with a predictive model of terminal state of charge (SOC) constraint. The predictive model can reinforce the local feature of the terminal SOC constraint, by a piecewise nonlinear regression model considering the stochastic vehicle mass. Especially, the stochastic vehicle mass is designed as stochastic variable at each road segment (the route between neighbored bus stops), and Optimal Latin Hypercube Design algorithm is deployed to sample and probe the design space constituted by the stochastic variables. Besides, the predictive model is constructed by partial least squares method, based on the sampled design space and corresponding optimal SOCs from dynamic programming. Simulation results show that the proposed predictive model is reasonable and can optimally predict the terminal SOC constraint at every receding horizon. Furthermore, although the fuel economy of the RHC strategy is worse than the DP strategy, it can be improved by 5.29% at least, compared to the rule-based strategy.