EIS curve fitting ECM extraction and SoH calculation

Nyquist Plot with Genetic Algorithm Fitting

Nyquist Plot

Enter Circuit Parameters

R₁:

R₂:

C₁:

R₃:

W₁:

Phase W₁ (Fixed):

C₂:

Upload EIS Data (.csv)

State of Charge (SoC %): 90% 70% 50% 30% 10%

Choose file

Fitted Parameters for Each SoC

SoC (%)	R1	R3	W1	C2

State of Health (SOH) Result

SOH(%): N/A

Test files in the "How It Works" section

This app demonstrates the Electrochemical Impedance Spectroscopy (EIS) data processing and the fitting of the electrochemical data, particularly for Lithium-ion Capacitors (LICs) cell used in [1], using Nyquist plots and Genetic Algorithms. Through the data processing the equivalent circuit elements (R1,R2,C1,R3,W and C2) as shown in the figure are extracted. EIS is employed to diagnose degradation and estimate the State of Health (SOH) of LICs, as outlined in the attached paper.

Key Concepts:

• EIS: A method that measures impedance to analyze internal processes in energy storage systems like LICs.
• SOH Estimation: LICs degrade over time, affecting their capacity and performance. SOH helps monitor their health in real-time.
• Nyquist Plot: This graphical tool helps in visualizing the impedance data collected via EIS, showing how the internal resistance changes with aging.

How It Works:

1. Upload Experimental Data: You can upload EIS data in .csv format.

LiC90%SoC  LiC70% SoC  LiC50% SoC LiC30% SoC LiC10% SoC

1. Plot the Data: The app plots a Nyquist curve based on the uploaded data and circuit parameters.
2. Fit the Curve: Using Genetic Algorithms, the app fits a theoretical model to your experimental data, optimizing it to match key parameters like resistance and capacitance.
3. Analyze SOH: By fitting the data and adjusting circuit parameters, the app estimates the SOH of LICs after having 5 different EIS at different 5 SOC.

For any questions you are welcome to send it to Dr.Tarek at  tarekm@ieee.org 

[1]  T. Ibrahim, M. U. Tahir, V. Knap, and D. I. Stroe, "Degradation Analysis of Lithium-ion Capacitors based on Electrochemical Impedance Spectroscopy," 2024 IEEE Energy Conversion Congress and Exposition (ECCE), Phoenix, AZ, USA, Oct. 20-24, 2024

Tool created at Enginius.org