Real-Time Estimation of PEMFC Parameters Using a Continuous-Discrete Extended Kalman Filter Derived from a Pseudo Two-Dimensional Model

Abstract

Proton Exchange Membrane Fuel Cells (PEMFCs) are clean energy conversion devices that are widely used in various energy applications. In most applications, the main challenge is accurately estimating the state of health (SoH) of the PEMFCs during dynamic operating conditions. Moreover, their behavior is affected by numerous physical phenomena such as heat and membrane flooding. This paper proposes the design of an observer to estimate the PEMFC parameters. A state-space model is first built from 2D physical equations solved by a finite difference in a discretized space domain. The discretized dynamic model is then used to design an observer based on the continuous-discrete extended Kalman filter. The observer has been validated experimentally and is used to estimate the parameters of a PEMFC under dynamic operating conditions. For several load variations, the results obtained using the proposed observer accurately characterize the dynamic responses of PEMFC in real-time.

A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery

“…Electrochemical models analyze the internal reaction mechanism and external characteristics of lithium-ion batteries by constructing multiple sets of partial differential equations, so as to describe the ion movement in the battery. Electrochemical models are generally classified into pseudo-two-dimensional models [6] and single-particle models [7,8]. In [9], an electrochemical model is established to identify the thermodynamic attributes of capacity loss.…”