A Novel Energy Management Strategy for Plug-in Hybrid Electric Buses Based on Model Predictive Control and Estimation of Distribution Algorithm

Energy management strategies determine how much energy is consumed by the engine and electric motor of plug-in hybrid electric buses (PHEBs), which represent critical fuel-saving technologies. In this study, a model predictive control (MPC) method with the estimation of distribution algorithm (EDA) as the solver is proposed to optimize the energy flow of PHEBs. Inspired by the recursive mechanism, short-term velocity prediction is achieved based on a Markov chain model with online updates to greatly improve prediction accuracy. Then, the energy-flow control problem of PHEBs is formulated as a discrete-time nonlinear optimization problem. Due to its strong nonlinear multivariable and constrained nature, the control algorithm is implemented by using MPC. To obtain an optimal solution efficiently, the EDA algorithm is incorporated into the MPC-based control framework, in which the Gaussian distribution is selected as a probabilistic model to characterize the candidate solutions and make full use of the statistical information extracted from the search experience. All performance verifications were conducted by theoretical simulation and hardware-in-the-loop. The verification results show that the proposed strategy can greatly improve the fuel economy and the shorten computational time over cycle-based driving.

Automated summary

This study proposes a model predictive control method with an estimation of distribution algorithm to optimize the energy flow of plug-in hybrid electric buses (PHEBs). The short-term velocity prediction is achieved using a Markov chain model with online updates, and the energy-flow control problem is formulated as a discrete-time nonlinear optimization problem. The EDA algorithm is incorporated into the MPC-based control framework to efficiently obtain an optimal solution.