Abstract

Electrochemical impedance spectroscopy (EIS) is a powerful tool for investigating electrochemical systems, such as lithium-ion batteries or fuel cells, given its high frequency resolution. The distribution of relaxation times (DRT) method offers a model-free approach for a deeper understanding of EIS data. However, in lithium-ion batteries, the differential capacity caused by diffusion processes is non-negligible and cannot be decomposed by the DRT method, which limits the applicability of the DRT method to lithium-ion batteries. In this study, a joint estimation method with Tikhonov regularization is proposed to estimate the differential capacity and the DRT simultaneously. Moreover, the equivalence of the differential capacity and the incremental capacity is proven. Different types of commercial lithium-ion batteries are tested to validate the joint estimation method and to verify the equivalence. The differential capacity is shown to be a promising approach to the evaluation of the state-of-health (SOH) of lithium-ion batteries based on its equivalence with the incremental capacity.

Highlights

  • Electrochemical impedance spectroscopy (EIS) has been proven to be a powerful tool for the diagnosis of complex electrochemical systems, including lithium-ion batteries [1,2,3,4,5,6], fuel cells [7,8], and supercapacitors [9,10]

  • Some non-ideal processes and the overlapping effects lead to a certain level of ambiguity of the equivalent-circuit model (ECM) during the model identification [25,26,27]

  • This problem needs to be settled by the deconvolution of the EIS data with respect to the distribution of relaxation times (DRT) [28,29,30,31,32]

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Summary

Introduction

Electrochemical impedance spectroscopy (EIS) has been proven to be a powerful tool for the diagnosis of complex electrochemical systems, including lithium-ion batteries [1,2,3,4,5,6], fuel cells [7,8], and supercapacitors [9,10]. Some non-ideal processes and the overlapping effects lead to a certain level of ambiguity of the ECM during the model identification [25,26,27]. This problem needs to be settled by the deconvolution of the EIS data with respect to the distribution of relaxation times (DRT) [28,29,30,31,32]

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Results
Conclusion

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