Improving the Performance of a Dual Kalman Filter for the Identification of PEM Fuel Cells in Impedance Spectroscopy Experiments

Abstract

In this paper, the Dual Kalman Filter (DKF) is used for the parametric identification of an RC model of a Polymer Electrolyte Membrane Fuel Cell (FC) stack. The identification is performed for diagnostic purposes, starting from time-domain voltage and current signals in the framework of Electrochemical Impedance Spectroscopy (EIS) tests. Here, the sinusoidal input of the tests makes the identification of DKF parameters challenging. The paper analyzes the filter performance and proposes a possible approach to address the filter tuning to let it work with FC operating either in normal conditions or in the presence of drying and flooding fault conditions, or in fuel starvation mode. The analysis is mainly performed in a simulated environment, where the Fouquet model is used to simulate the FC. Some criteria to tune the filter are derived from the analysis and used also with experimental data produced by some EIS tests, to achieve the best estimate in constrained conditions. The results show that the DKF can be turned into a valuable tool to identify the model parameters even with signals developed for other scopes. The identification results envisage the possibility of assisting the model-based FC diagnosis by means of a very simple tool that can run on a low-cost embedded device. Indeed, the simplicity of the filter approach and a lightweight implementation allow the deployment of the algorithm in embedded solutions.

Estimating PEMFC ohmic internal impedance based on indirect measurements

“…Kalman filter method is a better algorithm to estimate some important parameters indirectly measured. Those optimal algorithms (extended Kalman filter, double Kalman filter, etc) based upon the Kalman filter are also suitable to obtain these parameters and for real‐time system 8,9 . But there are some disadvantages about the application of Kalman filter method.…”

Multi-Fault Diagnosis of Interacting Multiple Model Batteries Based on Low Inertia Noise Reduction

“…In the actual BMS, battery failures are diverse, and different failure states need to be detected through various parameter changes, presenting a high degree of complexity. For example, electrolyte leakage will change the polarization parameters of the battery [19], and an aging battery will increase the ohmic resistance [20]. When a single Kalman Filter handles multiple fault states, it can only judge whether the battery is faulty but cannot locate and separate multiple faults.…”

Research on interactive multi‐model fault diagnosis method of Li‐ion battery based on noise suppression

“…In the actual BMS, battery failures are diverse, and different failure states need to be detected through various parameter changes, presenting a high degree of complexity. For example, electrolyte leakage will change the polarization parameters of the battery, 20 and an aging battery will increase the ohmic resistance. 21 When a single Kalman filter handles multiple fault states, it can only judge whether the battery is faulty but cannot locate and separate multiple faults.…”