An Online Identification based Energy Management Strategy for a Fuel Cell Hybrid Electric Vehicle

被引：1

作者：

Noura, Nassim ^{[1
]}

Boulon, Loic ^{[1
]}

Jemei, Samir ^{[2
,3
]}

机构：

[1] Univ Quebec Trois Rivieres, Hydrogen Res Inst, Trois Rivieres, PQ, Canada

[2] Univ Bourgogne Franche Comte, CNRS, FEMTO ST, Rue Thierry Mieg, F-90010 Belfort, France

[3] Univ Bourgogne Franche Comte, CNRS, FCLAB, Rue Thierry Mieg, F-90010 Belfort, France

来源：

2019 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC) | 2019年

基金：

加拿大自然科学与工程研究理事会;

关键词：

State of Charge; State of Health; Battery; Recursive Least Square; EMR; LITHIUM-ION BATTERIES; STATE; MODEL; SYSTEM; DEGRADATION; VOLTAGE; CHARGE;

D O I：

10.1109/vppc46532.2019.8952320

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper aims to present an accurate Energy Management Strategy (EMS) for a Fuel Cell Hybrid Electric Vehicle (FC-HEV). Batteries and FC performances are constantly changing with aging, temperature and so on. In order to ensure the accuracy of the EMS the parameters of those models are constantly updated thanks to an online identification. Adaptive filtering, based on Recursive Least Square (RLS) algorithm, is used to estimate the State of Health (SOH) and the State of Charge (SOC) of the battery in one hand. On the other hand The RLS algorithm estimates the polarization curve of the Fuel Cell. The impact of those estimated parameters on the EMS is shown through simulation.

引用

页数：5

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