A simplified fractional order impedance model and parameter identification method for lithium-ion batteries

Area of Science:

• Electrochemistry
• Battery Technology
• Computational Modeling

Background:

• Accurate internal parameter identification is crucial for evaluating lithium-ion battery performance.
• Existing models and algorithms may lack the precision required for comprehensive battery analysis.

Purpose of the Study:

• To develop a simplified fractional order impedance model for lithium-ion batteries.
• To establish an effective parameter identification method using a least square genetic algorithm.

Main Methods:

• Analysis of electrochemical impedance spectroscopy and transient response data.
• Development of a simplified fractional order impedance model.
• Application of the least square genetic algorithm with time-domain test data.
• Establishment of an equivalent tracking system for parameter identification.

Main Results:

• The proposed model and parameter identification method were verified through experiments and simulations.
• The method demonstrated high accuracy, with a maximum battery voltage tracing error within 0.5%.
• Compared to 2-RC and recursive least squares methods, smaller voltage fluctuations were observed.

Conclusions:

• The simplified fractional order impedance model is effective for lithium-ion battery parameter identification.
• The least square genetic algorithm efficiently estimates internal battery parameters.
• The developed method offers improved accuracy and performance for battery evaluation.