Non-iterative parameter identification for lithium-ion battery kinetics across multiple frequency ranges via electrochemical impedance model

Accurate and efficient parameter identification is essential for lithium-ion battery management and detection applications, as it enables precise state estimation, fault diagnosis, and quality assessment. However, conventional iterative identification methods suffer from long computation times, while deep learning-based approaches require extensive training, making them impractical for applications demanding rapid and real-time parameter acquisition. To address these challenges, this paper proposes a novel EIS-based non-iterative parameter identification method, which directly maps kinetic parameters to impedance characteristics using a simplified electrochemical impedance model. By eliminating iterative calculations and training overhead, the proposed method significantly improves identification speed while maintaining high accuracy. Experimental results show that it achieves a relative mean absolute error below 1 % and 6 % for negative and positive electrode impedance simulations, respectively. Furthermore, parameter consistency is validated by applying the identified parameters to time-domain electrochemical model simulations, yielding a root mean square error of 1.63 mV under high-frequency custom pulse conditions and 34.07 mV under dynamic stress test conditions. These results demonstrate the method's strong potential for real-time battery state estimation and efficient parameter identification in battery management systems, as well as rapid quality assessment in production lines.