Noise reduction with recursive filtering for more accurate parameter identification of electrochemical sources and interfaces

Simić, Mitar (author), Medić, Milan (author), Radovanović, Milan (author), Risojević, Vladimir (author), Bulić, Patricio (author)

Noise reduction is essential in analyzing electrochemical impedance spectroscopy (EIS) data for accurate parameter identification of models of electrochemical sources and interfaces. EIS is widely used to study the behavior of electrochemical systems as it provides information about the processes occurring at electrode surfaces. However, measurement noise can severely compromise the accuracy of parameter identification and the interpretation of EIS data. This paper presents methods for parameter identification of Randles (also known as R-RC or 2R-1C) equivalent electrical circuits and noise reduction in EIS data using recursive filtering. EIS data obtained at the estimated characteristic frequency is processed with three equations in the closed form for the parameter estimation of series resistance, charge transfer resistance, and double-layer capacitance. The proposed recursive filter enhances estimation accuracy in the presence of random noise. Filtering is embedded in the estimation procedure, while the optimal value of the recursive filter weighting factor is self-tuned based on the proposed search method. The distinguished feature is that the proposed method can process EIS data and perform estimation with filtering without any input from the user. Synthetic datasets and experimentally obtained impedance data of lithium-ion batteries were successfully processed using PC-based and microcontroller-based systems.

• Language: English
• Keywords: electrochemical impedance spectroscopy, R-RC circuit, recursive filter, equivalent circuit model, noise reduction, lithium-ion batteries
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 17 str.
• Numbering: Vol. 25, iss. 12, art. 3669
• PID: 20.500.12556/RUL-169866
• UDC: 621.35:004
• ISSN on article: 1424-8220
• DOI: 10.3390/s25123669
• COBISS.SI-ID: 239220995
• Publication date in RUL: 13.06.2025

Record is a part of a journal

• Title: Sensors
• Shortened title: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• COBISS.SI-ID: 10176278

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: elektrokemijska impedančna spektroskopija, R-RC vezje, rekurzivni filter, ekvivalentni model vezja, zmanjševanje šuma, litij-ionske baterije

Projects

• Funder: MESTD - Ministry of Education, Science and Technological Development of Republic of Serbia
• Project number: 9.032/961-69/24
• Name: Signal Processing Using Embedded Systems and Machine Learning

• Funder: MESTD - Ministry of Education, Science and Technological Development of Republic of Serbia
• Project number: 19/6-020/966-3-1/23
• Name: High-Performance Computing for Signal Processing