Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Dynamic Parameter Estimation of an Alkaline Electrolysis System
  Based on Bayesian Inference   When directly coupled with fluctuating energy sources such as wind and
photovoltage power, the alkaline electrolysis (AEL) in a power-to-hydrogen
(P2H) system is required to operate flexibly by dynamically adjusting its
hydrogen production rate. The flex-ibility characteristics, e.g., loading range
and ramping rate, of an AEL system are significantly influenced by some
parameters re-lated to the dynamic processes of the AEL system. These
parame-ters are usually difficult to measure directly and may even change with
time. To accurately evaluate the flexibility of an AEL system in online
operation, this paper presents a Bayesian Inference-based Markov Chain Monte
Carlo (MCMC) method to estimate these parameters. Meanwhile, posterior joint
probability distribu-tions of the estimated parameters are obtained as a
byproduct, which provides valuable physical insight into the AEL systems.
Experiments on a 25 kW electrolyzer validate the proposed pa-rameter estimation
method.

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The categories of this paper are: stat
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
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The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
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The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
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The categories of this paper are: econ
5
The categories of this paper are: gr-qc