Research on PEMFC cathode circulation under low-load conditions and its optimal control in FCV power system for long-term durability

Liu, Ze; Xu, Sichuan; Shi, Lei

doi:10.1016/j.ijhydene.2024.02.254

Cited by 1 publication

(1 citation statement)

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“…In contrast, the grey box model taking account of semi-empirical and semi-mechanical equations is modeled at a simpler electrochemical and physical level. It can emulate the behavior of fuel cells at various operation conditions and has attracted widespread attention [11]. The polarization curves (steady-state generalized electrochemical models) of PEMFC proposed by Amphlett et al are the appropriate nonlinear models for emulating electrochemical property under steady-state operation [12].…”

Section: Introductionmentioning

confidence: 99%

An Accurate Parameter Estimation Method of the Voltage Model for Proton Exchange Membrane Fuel Cells

Mei,

Meng,

Tang

et al. 2024

Energies

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Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.

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Section: Introductionmentioning

confidence: 99%