Abstract:

Analyzing the behavior of a battery, especially its impedance parameters and functioning under varied conditions, can be performed using the methods of electromagnetic modeling. Approaches like these aid in understanding intricacies internal to battery cells themselves, such as how cells respond to input signal oscillations at different electrical frequencies. Bound techniques today make it almost impossible to achieve precision with taking battery impedance values, especially in reactive cases where there are rapid shifts and complex inter electrochemical reactions take place. Conventional methods tend to oversimplify these scenarios due to lack of precision capturing dynamics, which might lead to over or under estimating battery life diagnostics. The framework presented here addresses these problems through the finite element analysis (FEM) approach, incorporating the Simulation of Lithium-Ion Battery Cell Impedance under Varying Frequencies. This method achieves optimal effect by integrating these electromagnetic interactions substantially dynamically to the region of interest volume. Depicting the galvanostatic and impedance response of a battery over different frequencies while maintaining their active interactions enables achievement of design maximization and understanding of performance through the aid of FEM. Compared to other frameworks concentrated on modeling dynamic processes in the subsystem of the battery, this improves competition against other approaches by reducing costs of computation, thus shifting focus on battery lifetime and efficiency design strategies. With the use of the provided method, more precise scenarios encountered in reality can be modeled allowing accurate performance forecasts under differing conditions and contexts. The results show that the methods provided achieve a higher level of accuracy for predicting impedance and enhance the effectiveness of the information supplied for data optimization of the battery while also minimizing the previously existing model's variance. The proposed method secures the following results for the accuracy of impedance prediction: 98.3% and 97.6% for optimization of the battery and 34.5% for discrepancies.