Hybrid Energy Management of Solid Oxide Fuel Cell/Lithium Battery System

Dong Li; Xiaobo Zhong; Yuanwu Xu; Chongyang Wei; Song Zhao; Jianhua Jiang; Zhonghua Deng; Xiaowei Fu; Xi Li

doi:10.17775/CSEEJPES.2021.00640

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Hybrid Energy Management of Solid Oxide Fuel Cell/Lithium Battery System

Regular Papers | 更新时间：2026-02-06

- Hybrid Energy Management of Solid Oxide Fuel Cell/Lithium Battery System
- CSEE Journal of Power and Energy Systems Vol. 11, Issue 4, Pages: 1610-1624(2025)
- 作者机构：
  
  1. Key Laboratory of Image Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology,Wuhan,China
  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, College of Computer Science and Technology, Wuhan University of Science and Technology,Wuhan,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.00640
  CLC：
- Published：2025
- 稿件说明：
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Dong Li, Xiaobo Zhong, Yuanwu Xu, et al. Hybrid Energy Management of Solid Oxide Fuel Cell/Lithium Battery System[J]. CSEE Journal of Power and Energy Systems, 2025, 11(4): 1610-1624.
DOI：

Dong Li, Xiaobo Zhong, Yuanwu Xu, et al. Hybrid Energy Management of Solid Oxide Fuel Cell/Lithium Battery System[J]. CSEE Journal of Power and Energy Systems, 2025, 11(4): 1610-1624. DOI： 10.17775/CSEEJPES.2021.00640.

摘要

Abstract

The research of the fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission. However

the lower efficiency of energy management has been a critical factor that obstructs the commercialization of the hybrid system. In this study

based on finite element ideas

a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell. As for the lithium battery

previous energy management studies generally only focused on the state of charge

however

the state of health is also a key parameter for lithium batteries. In this paper

considering the strong coupling between variables

we use a particle filter algorithm to jointly estimate the state of charge and state of health. To reasonably distribute and manage the energy in the hybrid system

a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery. Finally

our algorithm is verified by using the DSPACE simulation platform. The results show that the hybrid system with an energy management strategy not only meets the real-time sharply changing demands

but also ensures the hybrid system is working efficiently and safely.

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