Identification Methods for Lithium Battery RC Equivalent Circuit Models with Code Implementation Approaches

The lithium battery RC equivalent circuit model serves as a crucial tool for studying battery dynamic characteristics, where the dual-RC model provides more accurate representation of battery polarization effects. This model utilizes resistor-capacitor networks to simulate the battery's dynamic response process. In MATLAB implementations, engineers typically define the RC network topology using Simulink blocks or state-space equations to capture voltage relaxation behaviors.

When constructing dual-RC models, three core challenges must be addressed: first, determining the model structure by selecting appropriate numbers of parallel RC branches; second, choosing parameter identification methods that directly impact model accuracy; finally, implementing model validation procedures to verify reliability through experimental data. Code implementation often involves creating parameter estimation scripts that interface with battery test equipment via GPIB or CAN bus communication protocols.

Common parameter identification methods include optimization algorithms like least squares fitting and particle swarm optimization. These techniques extract model parameters by fitting charge-discharge curves, where MATLAB's System Identification Toolbox provides built-in functions such as 'pem' for prediction error minimization. Compared to single-RC models, dual-RC models more accurately represent medium-to-short-term dynamic characteristics but require higher precision in parameter identification through iterative optimization routines.

Practical applications must consider temperature effects on model parameters and model applicability under different operating conditions. Developers often incorporate temperature compensation algorithms using lookup tables or polynomial functions. Through proper parameter identification and model validation incorporating statistical metrics like RMSE, dual-RC equivalent models can effectively predict lithium battery dynamic behavior across various usage scenarios.