Model Predictive Online Trajectory Planning for Adaptive Battery Discharging in Fuel Cell Vehicle

TL;DR

This paper introduces an online trajectory planning method for real-time coordination of fuel cell and battery systems in plug-in hybrid electric vehicles, optimizing energy use under uncertain driving conditions.

Contribution

It develops an iterative LQR-based online planner that explicitly and adaptively manages battery SOC and fuel cell responses, filling a gap in dynamic energy management research.

Findings

Effective coordination of FC and battery demonstrated in simulations.

Adaptive SOC trajectory planning improves energy efficiency.

Hierarchical approach integrates high-level planning with real-time control.

Abstract

This paper presents an online trajectory planning approach for optimal coordination of Fuel Cell (FC) and battery in plug-in Hybrid Electric Vehicle (HEV). One of the main challenges in energy management of plug-in HEV is generating State-of-Charge (SOC) reference curves by optimally depleting battery under high uncertainties in driving scenarios. Recent studies have begun to explore the potential of utilizing partial trip information for optimal SOC trajectory planning, but dynamic responses of the FC system are not taken into account. On the other hand, research focusing on dynamic operation of FC systems often focuses on air flow management, and battery has been treated only partially. Our aim is to fill this gap by designing an online trajectory planner for dynamic coordination of FC and battery systems that works with a high-level SOC planner in a hierarchical manner. We propose an iterative LQR based online trajectory planning method where the amount of electricity dischargeable at each driving segment can be explicitly and adaptively specified by the high-level planner. Numerical results are provided as a proof of concept example to show the effectiveness of the proposed approach.