Nonlinear modeling of PEMFC based on fractional order subspace identification

Abstract

Aiming at the multivariable, nonlinear and fractional‐order characteristics of proton exchange membrane fuel cell (PEMFC), this paper presents a nonlinear state space model based on a novel fractional Hammerstein model subspace identification theory. To reduce the complexity of modeling and choose the suitable input variables, canonical correlation analysis (CCA) method is used to select the most influential factors as the model input variables, and correlation analysis (CA) method is employed to remove the redundant input variables. To guarantee that the input‐output data are derivable at different fractional order, a Poisson moment function (PMF) is employed to construct the fractional order Hammerstein model with a six‐order polynomial as the front static nonlinear unit. To improve computing speed, a fractional differential short memory method (SMM) is proposed to reduce the computation cost of the identification algorithm. Meanwhile, a fuzzy genetic algorithm is adopted to acquire the best fractional order. Simulation results show that the fractional subspace identifying method can avoid fuel cell's internal complexity and PEMFC identification model can describe the working process of PEMFC accurately and quickly, which will provide an ideal control model for some advanced controller.

Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

“…This tuning step is iteratively performed until the last iteration. This section also explains the Hammerstein model that is widely used to identify real nonlinear plants and processes [16], [17], including Thermoelectric cooler [18], [19], amplified piezoelectric tube actuators [20], forecast of wind speed [21], giant magnetostrictive actuators (GMAs) [22], boiler superheated steam pressure [23], boost converter of DC/DC [24], intelligent pneumatic actuator (IPA) [25], hydraulic mini excavator [26], ultrasonic motor [27], proton exchange membrane fuel cell (PEMFC) [28], turntable servo system [29], valve Stiction [30] and electrically stimulated muscle [31]. The Hammerstein model can accurately model actuators to solve control issues and strengthen nonlinearity system identification.…”

“…They employed the GA-based nonlinear model predictive control (GA-NMPC) method to demonstrate the applicability of the Hammerstein model for control design. Another plant called proton exchange membrane fuel cell (PEMFC) is modelled in [28] based on the fractional Hammerstein model and a fuzzy GA is adopted to acquire the best fractional order of the Hammerstein system. Next, a nonlinear multi-input single-output (MISO) system identification method using Hammerstein model was presented in [40], which involved fractional transfer function and modified radial base function NN (MRBFNN) as the linear dynamic component and a static nonlinear subsystem, in which modified GA defined the fractional orders, as well as the width and centre of MRBFNN, apart from identifying the initial prediction of other unidentified parameters.…”

Identification of Fractional Order System with Scarce Measurement Data Based on Multi Innovation Estimation Algorithm

“…In the past decades in system modeling and control, fractional order systems have attracted continually an increasing interesting among researchers [20][21][22]. The most commonly used in discrete cases are GL fractional calculus [20] , RL fractional calculus [21] and Caputo fractional calculus [22] , which is used in this paper is the definition of GL calculus is expressed as follows:…”

Adaptive integral type‐terminal sliding mode control for PEMFC air supply system using time delay estimation algorithm

“…Among the different kinds of fuel cells, the proton exchange membrane fuel cell (PEMFC) systems are candidates for both portable and stationary applications thanks to low pollution, low operation temperature, low weight, high efficiency and fast in starting time. PEMFCs provide electricity and water through the electrochemical reaction from oxygen and hydrogen [5][6][7].…”