Bayesian Approach to Estimate Distribution of Relaxation Times for Lithium-Ion Batteries

Abstract

Electrochemical impedance spectroscopy (EIS) has emerged as a powerful tool for analyzing the state of lithium-ion batteries due to its ability to provide rich electrochemical information. Traditionally, equivalent circuit and physical models have been utilized for interpreting EIS data. However, these methods possess inherent drawbacks: equivalent circuit models are non-unique, while physical models can be challenging to implement. As an alternative, the distribution of relaxation times (DRT) has garnered increasing attention in recent years. The DRT method enables the analysis of EIS data without predefining a model's shape, allowing for the decomposition of internal characteristics in complex systems, such as lithium-ion batteries. Despite its advantages, DRT estimation is an ill-posed inverse problem that often requires regularization or fine-tuning based on empirical choices, which can lead to the misinterpretation of EIS data.To address these challenges, we propose a Bayesian approach that enables the estimation of DRT while considering uncertainties, even in the presence of observational errors in EIS data. Moreover, the proposed method can assist in estimating the state of health (SOH) of the battery, which provides valuable insights into the battery's overall performance, longevity, and safety. To validate the efficacy of our proposed method, we apply it to synthetic EIS data and compare its performance with existing DRT estimation methods. The results show that the proposed Bayesian approach offers a more robust and accurate estimation of DRT. We also demonstrate the practical applicability of the proposed method by applying it to real lithium-ion battery EIS experimental data, where it successfully extracts meaningful information about the battery's internal state and SOH. In summary, the proposed DRT-based Bayesian method has the potential to enable a robust diagnosis of the internal state and SOH of lithium-ion batteries through EIS measurements. By incorporating this approach, researchers and industry practitioners can gain a deeper understanding of battery performance, facilitating the development of improved diagnostic techniques and battery management strategies to enhance the performance and reliability of lithium-ion batteries. This, in turn, could contribute to the broader adoption of sustainable energy solutions, such as electric vehicles and renewable energy storage systems.This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grants funded by the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20214910100070). Figure 1