Reconstruction of the incremental capacity trajectories from current-varying profiles for lithium-ion batteries

Summary

The reliable assessment of battery degradation is fundamental for safe and efficient battery utilization. As an important in situ health diagnostic method, the incremental capacity (IC) analysis relies highly on the low-noise constant-current profiles, which violates the real-life scenarios. Here, a model-free fitting process is reported, for the first time, to reconstruct the IC trajectories from noisy or even current-varying profiles. Based on the results from overall 22 batteries with three case studies, the errors of the peak positions in the reconstructed IC trajectories can be bounded within only 0.25%. With health indicators extracted from the reconstructed IC trajectories, the state of health can be readily determined from simple linear mappings, with estimation error lower than 1% only. By enabling the IC-based methods under complex load profiles, enhanced health assessment could be implemented to improve the reliability of the power systems and further promoting a more sustainable society.

Graphical abstract

Highlights

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  Reconstruct the incremental capacity trajectories from non-constant-current profiles

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  The positioning error of all incremental capacity peaks could be bounded by 0.25%

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  Suitable for single cell/pack with different temperatures, aging, and current rates

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  Developing a shape-preserving transfer learning network for the reconstruction

Abstract

Electrochemical energy production; Energy storage; Energy materials