Multiobjective Optimal Predictive Energy Management for Fuel Cell/Battery Hybrid Construction Vehicles

Abstract

Fuel cell/battery hybrid construction vehicles (FCHCVs) have shown great promise; however, the complex working conditions of construction vehicles pose considerable challenges to the performance and energy management of a fuel cell/battery hybrid system. In this paper, multiobjective optimal model predictive control (MOMPC)-based energy management for FCHCVs is explored. A system model is established that includes an economic model and a lifetime model. In the MOMPC framework, multiobjective optimization is conducted to enhance fuel cell durability and battery lifetime while minimizing costs. Since the energy management problem is a nonlinear problem with hard state constraints, it can be difficult to resolve online. The multiobjective approach employs an adaptive weight-adjustment method based on a fuzzy logic algorithm. An economic evaluation of the FCHCV is conducted over its life cycle with respect to the power source size. Simulation results indicate economic savings and prolonged battery lifetime with the MOMPC-based strategy, compared with conventional benchmarks.

Keywords

• Model predictive control
• multiobjective optimization
• energy management
• fuel cell
• construction vehicle