Hybrid model predictive power flow control of a fuel cell-battery vehicle

Abstract

This paper considers optimal power flow control of a fuel cell-battery hybrid vehicle (FCHV) powertrain having three distinct modal configurations (modes): electric motor propelling/battery discharging, propelling/charging, and generating/charging. Each mode has a distinct set of dynamics and constraints. Using component dynamical/algebraic models appropriate to power management, the paper develops a supervisory-level switched system model as an interconnection of subsystems. Given the model, the paper sets forth a hybrid model predictive control strategy based on a minimization of a performance index (PI) that trades off tracking and fuel economy in each operational mode. Specifically the PI trades off velocity tracking error, battery state of charge variance, and hydrogen usage while penalizing frictional braking. The optimization is performed using an embedded system model and collocation with MATLAB's fmincon to compute mode switches and continuous time controls thereby avoiding the computational complexity of alternate approaches based on, e.g., mixed integer programming. To demonstrate the approach, an example FCHV following trapezoidal and sawtooth drive profiles is simulated. PI weights are varied for reduced hydrogen use and higher final battery charge to illustrate various performance trade-offs.