Research on Energy Management Strategy of Extended-Range Fuel Cell Vehicle

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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Optimization of fuzzy control energy management strategy for fuel cell vehicle power system using a multi‐islandgenetic algorithm

Energy Science & Engineering ◽

10.1002/ese3.835 ◽

2020 ◽

Author(s):

Zhen Zhao ◽

Tie Wang ◽

Meng Li ◽

Heng Wang ◽

Yiquan Wang

Keyword(s):

Fuel Cell ◽

Fuzzy Control ◽

Power System ◽

Energy Management ◽

Management Strategy ◽

Fuel Cell Vehicle ◽

Energy Management Strategy

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Design and Validation of Energy Management Strategy for Extended-Range Fuel Cell Electric Vehicle Using Bond Graph Method

Energies ◽

10.3390/en14020380 ◽

2021 ◽

Vol 14 (2) ◽

pp. 380

Author(s):

Ke Song ◽

Yimin Wang ◽

Cancan An ◽

Hongjie Xu ◽

Yuhang Ding

Keyword(s):

Graph Theory ◽

Fuel Cell ◽

Energy Management ◽

Management Strategy ◽

Bond Graph ◽

Fuel Cell Vehicles ◽

Energy Management Strategy ◽

Powertrain System ◽

Extended Range ◽

Bond Graph Theory

In view of the aggravation of global pollution and greenhouse effects, fuel cell electric vehicles (FCEVs) have attracted increasing attention, owing to their ability to release zero emissions. Extended-range fuel cell vehicles (E-RFCEVs) are the most widely used type of fuel cell vehicles. The powertrain system of E-RFCEV is relatively complex. Bond graph theory was used to model the important parts of the E-RFCEV powertrain system: Battery, motor, fuel cell, DC/DC, vehicle, and driver. In order to verify the control effect of energy management strategy (EMS) in a real-time state, bond graph theory was applied to hardware-in-the-loop (HiL) development. An HiL simulation test-bed based on the bond graph model was built, and the HiL simulation verification of the energy management strategy was completed. Based on the comparison to a power-following EMS, it was found that fuzzy logic EMS is more adaptive to vehicle driving conditions. This study aimed to apply bond graph theory to HiL simulations to verify that bond graph modeling is applicable to complex systems.

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