Cost-Optimal Energy Management of Hybrid Electric Vehicles Using Fuel Cell/Battery Health-Aware Predictive Control

TLDR

Energy management improves the economy of fuel cell/battery hybrid electric vehicles, yet prior work largely ignores the impact of fuel cell and battery degradation on control objectives. This study proposes a cost‑optimal predictive energy management strategy that explicitly accounts for fuel cell and battery degradation, establishes an MPC framework to minimize total running cost, and examines how driving and pricing scenarios affect vehicle economy. The authors develop an MPC framework that minimizes total running cost—including hydrogen and degradation costs—and evaluate its performance across different prediction horizons and uncertainty levels.

Abstract

Energy management is an enabling technology for increasing the economy of fuel cell/battery hybrid electric vehicles. Existing efforts mostly focus on optimization of a certain control objective (e.g., hydrogen consumption), without sufficiently considering the implications for on-board power sources degradation. To address this deficiency, this article proposes a cost-optimal, predictive energy management strategy, with an explicit consciousness of degradation of both fuel cell and battery systems. Specifically, we contribute two main points to the relevant literature, with the purpose of distinguishing our study from existing ones. First, a model predictive control framework, for the first time, is established to minimize the total running cost of a fuel cell/battery hybrid electric bus, inclusive of hydrogen cost and costs caused by fuel cell and battery degradation. The efficacy of this framework is evaluated, accounting for various sizes of prediction horizon and prediction uncertainties. Second, the effects of driving and pricing scenarios on the optimized vehicular economy are explored.