Noise Reduction with Recursive Filtering for More Accurate Parameter Identification of Electrochemical Sources and...

Area of Science:

• Electrochemistry
• Electrical Engineering
• Data Analysis

Background:

• Electrochemical Impedance Spectroscopy (EIS) is crucial for studying electrochemical systems.
• Measurement noise in EIS data hinders accurate parameter identification and interpretation.
• Randles equivalent electrical circuits are commonly used to model electrochemical interfaces.

Purpose of the Study:

• To develop a noise reduction method for EIS data.
• To enable accurate parameter identification of Randles circuits.
• To present an automated and self-tuned filtering and estimation procedure.

Main Methods:

• Recursive filtering is applied to EIS data for noise reduction.
• Parameter estimation for series resistance, charge transfer resistance, and double-layer capacitance is performed using closed-form equations.
• An optimal recursive filter weighting factor is self-tuned via a search method.

Main Results:

• The proposed recursive filter significantly enhances estimation accuracy in the presence of random noise.
• The method successfully processed synthetic and experimental EIS data from lithium-ion batteries.
• Both PC-based and microcontroller-based systems demonstrated the effectiveness of the approach.

Conclusions:

• The presented method offers an automated and user-independent approach to noise reduction and parameter identification in EIS.
• This technique improves the reliability of electrochemical model parameter estimation.
• The findings are applicable to various electrochemical systems, including battery research.