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Explainable Artificial Intelligence of Things for Health Prognosis of Lithium-Ion Batteries

MCML Authors

Shanling Ji

→ Group Helge Stein
Digital Catalysis

Helge Stein

Prof. Dr.

Associate

Digital Catalysis

Abstract

Efforts to enhance the state of health (SOH) estimation for lithium-ion batteries have increasingly focused on diverse machine learning methods, especially with the promising artificial intelligence of things (AIoT) technologies. However, direct SOH prediction using electrical signals as inputs lacks an explanation of the aging mechanism evolution. This study proposes an explainable AIoT framework based on a physics-informed sequence-to-sequence (PIISeq2Seq) model to accurately estimate SOH. The obtained electrical signals are transmitted to the AIoT cloud system for parameter identification and SOH estimation. Moreover, the degradation pathways with different potential mechanisms for lab batteries with LiNiO2 cathodes and Si@C anodes are investigated by quantifying polarization processes. The robustness of PIISeq2Seq across different temperatures is further validated using a public data set of commercial batteries, achieving an mean absolute percentage error of 1.11% on lab cells and 1.06%–1.14% on commercial batteries. This work offers valuable insights for developing explainable approaches to battery SOH estimation, especially the explainable AIoT for real-time evaluation of the lithium-ion batteries in industrial automation.

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