Prognostics of a multistack PEMFC system with multiagent modeling

Abstract

Current research on prognostics of PEMFC mainly focuses on one stack. However, in practice, the PEMFC stacks are assembled together to provide higher and more reliable power. In this paper, an original multiagent approach is proposed for predicting the remaining useful life‐times of all stacks in a multistack PEMFC system. The predictions are updated dynamically by particle filtering when new stack loads are collected. The major contribution of the paper is not on degradation modeling of a single PEMFC, but on the cooperative prognostics of multiple PEMFC stacks in the same power system. A case study concerning a synthetic combined heat and power system with three PEMFC stacks is considered and the results show the effectiveness of the proposed approach.

An Echo State Network for fuel cell lifetime prediction under a dynamic micro-cogeneration load profile

“…The developed approach in [25] was incorporated in a multiagent system to estimate the RUL of a multistack PEMFC system [27]. Several PFs were used in the multi agent system.…”

A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells

“…In recent years, three types of PEMFC prognostic strategies [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][24][25][26][27][28][29][30][31][32][33] have been proposed: model-based, data-based, and hybrid methods based on model and data fusion. Among them, the model-based strategy describes the degradation processes by constructing a physical model [6][7][8], which is beneficial to the prediction accuracy in the condition that the physical degradation model is sufficiently accurate. The authors in [6], proposed a multi-agent prediction method based on particle filter (PF) for three parallel FC stacks.…”

“…Among them, the model-based strategy describes the degradation processes by constructing a physical model [6][7][8], which is beneficial to the prediction accuracy in the condition that the physical degradation model is sufficiently accurate. The authors in [6], proposed a multi-agent prediction method based on particle filter (PF) for three parallel FC stacks. In this work, only a simple degradation model is considered, and the RULs of three FC stacks were discussed without analyzing the real degradation trend.…”

“…This model estimated the health state and the RUL by improving the initial parameter setting of conventional unscented Kalman filter. The same defect as in [6] is that the prediction of the degradation trend was not considered.…”

Proton Exchange Membrane Fuel Cells Prognostic Strategy Based on Navigation Sequence Driven Long Short-term Memory Networks

“…Model-based methods describe the degradation of FCs by building analytical degradation models. The authors in [5], proposed a multi-agent prediction method based on particle filter (PF) for three parallel PEMFCs. However, only a physical model PF method was considered in this work.…”