Stochastic Predictive Energy Management of Power Split Hybrid Electric Bus for Real-World Driving Cycles

Abstract

The energy conversion efficiencies among different sources of power split hybrid electric vehicle rely on the energy management strategy. In this paper, the energy management of a power split hybrid electric bus (HEB) is described as the predictive control problem based on the linear control-oriented model of the HEB. In order to ensure that the engine can output the desired power, the fuzzy PI controller, which can realize the optimal engine speed tracking, is further designed. Two real-world driving cycles are analyzed and formulated to evaluate the vehicle fuel economy under transient practical conditions. On this basis, the driver torque demand and vehicle velocity in the prediction horizon are derived with the stochastic one-step Markov chain. Finally, the hardware-in-the-loop (HIL) simulation platform is built to explore the validity of the controller. Compared with the adaptive equivalent fuel consumption minimization strategy, HIL test results demonstrate the real-time capability and benefits of the proposed approach in optimizing the energy management of HEB.