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

Katsuya Shigematsu, Hikaru Hoshino, Eiko Furutani

TL;DR

The paper addresses energy management for plug-in HEVs with FC and battery under driving uncertainties by introducing a three-layer hierarchical framework that couples a high-level SOC planner with a middle-layer iLQR-based online trajectory planner. The middle layer explicitly constrains per-segment battery discharge (via a budget Q_max) to coordinate FC dynamics with SOC trajectories, and employs a variational-equation discretization to enable time-varying linearization for efficient iLQR optimization. Key contributions include reformulating the online planner to respect FC safety constraints (e.g., oxygen starvation) and safety limits while tracking power demand, and quantifying hydrogen savings as a function of the discharge budget, thus providing data to train SOC planners. The work demonstrates, in a proof-of-concept simulation, that the planner can adapt to transient loads and yield meaningful hydrogen-economy improvements, offering a pathway for stronger FC–battery coordination in real-world applications.

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.

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

TL;DR

The paper addresses energy management for plug-in HEVs with FC and battery under driving uncertainties by introducing a three-layer hierarchical framework that couples a high-level SOC planner with a middle-layer iLQR-based online trajectory planner. The middle layer explicitly constrains per-segment battery discharge (via a budget Q_max) to coordinate FC dynamics with SOC trajectories, and employs a variational-equation discretization to enable time-varying linearization for efficient iLQR optimization. Key contributions include reformulating the online planner to respect FC safety constraints (e.g., oxygen starvation) and safety limits while tracking power demand, and quantifying hydrogen savings as a function of the discharge budget, thus providing data to train SOC planners. The work demonstrates, in a proof-of-concept simulation, that the planner can adapt to transient loads and yield meaningful hydrogen-economy improvements, offering a pathway for stronger FC–battery coordination in real-world applications.

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.
Paper Structure (7 sections, 8 equations, 6 figures, 1 table)

This paper contains 7 sections, 8 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Model of Fuel Cell and Battery Systems
  3. Proposed Energy Management Strategy
  4. Overall Hierarchical Structure
  5. Online trajectory planner
  6. Simulation and Discussion
  7. Conclusions

Figures (6)

  • Figure 1: Schematic diagram of the fuel cell system
  • Figure 2: Proposed hierarchical energy management structure
  • Figure 3: Supplied power $P_\mathrm{sys}$ for each $Q_\mathrm{max}$ and the reference $P_\mathrm{ref}$
  • Figure 4: Time courses of the control input $u = [ v_\mathrm{cm},I_\mathrm{st},I_\mathrm{bat}]^\top$
  • Figure 5: Time course of the state $q_\mathrm{dis}$
  • ...and 1 more figures