Development of Energy Management Software Module for Fuel Cell Vehicle Based on AUTOSAR Methodology

Dynamic characteristics of a proton exchange membrane fuel cell (PEMFC) system can impact fuel economy and load following performance of a fuel cell vehicle, especially if those dynamics are ignored in designing top-level energy management strategy. To quantify the effects of fuel cell system (FCS) dynamics on optimal energy management, dynamic programming (DP) is adopted in this study to derive optimal power split strategies at two levels: Level 1, where the FCS dynamics are ignored, and Level 2, where the FCS dynamics are incorporated. Analysis is performed to quantify the differences of these two resulting strategies to understand the effects of FCS dynamics. While Level 1 DP provides significant computational advantages, the resulting strategy leads to load following errors that need to be mitigated using battery or FCS itself. Our analysis shows that up to 5% fuel economy penalty on New York city cycle (NYCC) and 3% on supplemental federal test procedure (US06) can be resulted by ignoring FCS dynamics, when the dominant dynamics of the FCS has settling time as slow as 8 seconds.

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Intelligent energy management of a fuel cell vehicle based on traffic condition recognition

Clean Technologies and Environmental Policy ◽

10.1007/s10098-016-1122-2 ◽

2016 ◽

Vol 18 (6) ◽

pp. 1945-1960 ◽

Cited By ~ 22

Author(s):

Mohsen Kandi Dayeni ◽

Mehdi Soleymani

Keyword(s):

Fuel Cell ◽

Energy Management ◽

Fuel Cell Vehicle ◽

Traffic Condition ◽

Condition Recognition

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Online Modeling of a Fuel Cell System for an Energy Management Strategy Design

Energies ◽

10.3390/en13143713 ◽

2020 ◽

Vol 13 (14) ◽

pp. 3713 ◽

Cited By ~ 2

Author(s):

Mohsen Kandidayeni ◽

Alvaro Macias ◽

Loïc Boulon ◽

João Pedro F. Trovão

Keyword(s):

Fuel Cell ◽

Energy Management ◽

Management Strategy ◽

Proton Exchange Membrane ◽

Cell System ◽

Proton Exchange ◽

Fuel Cell Vehicle ◽

Model Parameters ◽

Energy Management Strategy ◽

Initial Values

An energy management strategy (EMS) efficiently splits the power among different sources in a hybrid fuel cell vehicle (HFCV). Most of the existing EMSs are based on static maps while a proton exchange membrane fuel cell (PEMFC) has time-varying characteristics, which can cause mismanagement in the operation of a HFCV. This paper proposes a framework for the online parameters identification of a PMEFC model while the vehicle is under operation. This identification process can be conveniently integrated into an EMS loop, regardless of the EMS type. To do so, Kalman filter (KF) is utilized to extract the parameters of a PEMFC model online. Unlike the other similar papers, special attention is given to the initialization of KF in this work. In this regard, an optimization algorithm, shuffled frog-leaping algorithm (SFLA), is employed for the initialization of the KF. The SFLA is first used offline to find the right initial values for the PEMFC model parameters using the available polarization curve. Subsequently, it tunes the covariance matrices of the KF by utilizing the initial values obtained from the first step. Finally, the tuned KF is employed online to update the parameters. The ultimate results show good accuracy and convergence improvement in the PEMFC characteristics estimation.

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