Adaptive Parameter Identification of a Fuel Cell System for Health-Conscious Energy Management Applications

Abstract

Since a proton exchange membrane (PEM) fuel cell (FC) has time-varying characteristics, its online characteristics estimation (voltage, power, internal resistance, etc.) is becoming a key step in designing an energy management strategy (EMS) for hybrid FC vehicles. In this respect, this paper proposes a new method based on Lyapunov adaptation law to estimate the linear and nonlinear parameters of a renowned PEMFC model in the literature. Unlike most of similar estimators, the suggested approach determines the maximum current, which is a nonlinear parameter, online while guaranteeing the system closed-loop stability. This parameter is normally assumed to be constant while it changes through time owing to degradation and operating conditions variation. This alteration makes the model imprecise while extracting some important characteristics, such as maximum power and polarization curve. Therefore, it needs to be regularly updated along with other parameters. To demonstrate the capability of the suggested method, a detailed comparison is provided with the well-known extended Kalman filter (EKF) as an attested nonlinear estimator. Moreover, to highlight the effectiveness of the nonlinearity consideration, a comparison with KF is performed where the nonlinear parameter is considered constant. The performed experiments on a 500-W PEMFC show that the proposed method can be over twice as accurate as EKF and KF concerning the estimation of maximum power and current while its runtime is nearly half of them. Index Terms-Kalman filter, health assessment, Lyapunov stability, online modeling, fuel cell I. INTRODUCTION A. General context YBRID fuel cell (FC) vehicles are considered as one the most promising technical solutions in the battle to confront the climate change crisis [1]. The performance of this multiple energy source system highly depends on the design of an

Adaptive Nonlinear Parameter Estimation for a Proton Exchange Membrane Fuel Cell

“…where the unknown parameters are in a linear form with respect to the regressor. In order to achieve the parameter estimation for nonlinearly parameterized systems, we have to assume that the functions in ( 4) and ( 5) fulfill the convex condition, and thus the Taylor's expansion can be used to linearize the nonlinear functions around a local set [4], [24] as…”

“…When the system (22) satisfies Assumption 4, the nonlinearly parameterized function f m,k goes slower than the linearly parameterized function φ j θ j such that f m,k fulfills a global Lipschitz condition. For the PEMFC system with the unknown parameter vector θ studied in this paper, we can choose the following function D j such that condition (24) in Assumption 4 can be guaranteed [25]:…”

“…Then a set-based estimation method was used to estimate the uncertainty set and update the nominal parameter values. Recently, an adaptive parameter estimation with Taylor's expansion based local linearization was reported in [4], [24]. On the other hand, the monotonic nonlinearity is also beneficial to achieve the parameter convergence.…”

Simulation Approaches and Validation Issues for Open-Cathode Fuel Cell Systems in Manned and Unmanned Aerial Vehicles

“…The approach was extended in [110] and allowed the correction of the maximum current I max on the basis of variable operating conditions and degradation processes.…”

Analysis of the Path of Utilizing Big Data to Innovate Archive Management Mode to Enhance Service Capability

“…It is necessary to change the mindset of archive management from the ideological point of view. The service consciousness has a direct impact on the archive management service [19][20][21]. Apply big data technology in archive management work, combine the actual needs of archives users, strengthen the big data thinking and archive service consciousness of archive management personnel, build and establish modern archive management mode, especially to consciously transform their own archive service consciousness, change from traditional archive management consciousness to big data archive management consciousness, strengthen the application of big data technology in daily work, and consciously improve archive service.…”