From 603c18be52f821498333168ad59db5655ba39847 Mon Sep 17 00:00:00 2001
From: Gerardo Marx <gmarx_cc@itmorelia.edu.mx>
Date: Fri, 23 Jan 2026 09:39:41 -0600
Subject: [PATCH] adding suggested title and abstract

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 **The paper content must be filled here**
-**References, figures and format must be set in sharelatex site**
+**References, figures, and format must be set in sharelatex site**
+
+# Title
+Time‑Domain Identification of Electrolyzer Equivalent‑Circuit Parameters from Current Step Responses for Assessment Monitoring
+
 # Abstract (250–300 words); 
-# Key Words (5-7); 
-# Introduction, 
+
+Electrolyzer health monitoring is increasingly relevant for reliable hydrogen production under prolonged operation and dynamic loading. While electrochemical impedance spectroscopy (EIS) is widely used for diagnosing electrochemical systems, its implementation may be impractical for embedded or in-field monitoring. This work presents a time-domain system-identification approach to estimate the parameters of a simplified Randles equivalent circuit model using current transients induced by controlled-voltage-step excitations. The proposed model consists of an ohmic resistance in series with a parallel charge-transfer resistance and double-layer capacitance, enabling physically interpretable parameters ($R_\Omega$, $R_{ct}$, $C_{dl}$) linked to transport and interfacial dynamics. For each test, the cell voltage and current are sampled at a high rate around the step event, and a gray-box identification procedure fits the model parameters by minimizing the error between measured and simulated step responses. Parameter confidence is quantified through repeated excitations and residual analysis. The method is applied across multiple operating sessions to evaluate parameter drift as a function of electrolyzer use. Results show that the simplified Randles model reproduces the measured transients with low residual structure over short windows dominated by **first-order polarization dynamics**. Moreover, the estimated parameters exhibit repeatability within sessions and systematic changes across sessions consistent with progressive device use, supporting their role as degradation indicators. The presented framework provides a practical alternative for extracting diagnostic information from time-domain tests, facilitating implementation in low-complexity measurement setups and enabling periodic condition assessment without full impedance sweeps.
+
+
+# Key Words (5-7);
+- Electrolyzer monitoring
+- time-domain
+- system-identification
+- equivalent-circuit parameters
+- gray-box identification
+
+# Introduction
+Hydrogen represents a resource of significant interest within the energy industry because of its potential as a clean, sustainable, and storable energy source \cite{noor-azam2023}. Water electrolysis is a well-known electrochemical route in which direct current drives the decomposition of water into hydrogen at the cathode and oxygen at the anode using two electrodes and an ion-conducting electrolyte; in alkaline water electrolysis (AWE), ionic transport is enabled by concentrated alkaline solutions such as KOH or NaOH, offering advantages including flexibility, availability, and high hydrogen purity \cite{zouhri2016awe}. Nevertheless, despite its technical maturity, electrolysis is not yet widely implemented and accounts for only 4-5\% of global hydrogen production. This limited use of alkaline water electrolysis (AWE) for hydrogen generation can be attributed to the need for comprehensive polarization curves and validated electrochemical models, which are crucial for extrapolating laboratory data and forecasting electrolyzer voltage and hydrogen output at an industrial scale \cite{ssrn-4065639,zouhri2016awe}.
+
+The main idea is to link one of the most significant losses (ohmic) to the AWE-state-quality process for further control. Thus, a dynamic step response during at the beggining of the hydrogen production process can be used to determine problems in the system. 
+
 # Materials and Methods, 
 # Results and discussion, 
 # Conclusions;