Deep Learning Powered Online Battery Health Estimation Considering Multi-timescale Ageing Dynamics and Partial Charging Information

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6 Scopus Citations
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Original languageEnglish
Journal / PublicationIEEE Transactions on Transportation Electrification
Publication statusOnline published - 5 Apr 2023

Abstract

Online accurate battery state-of-health (SOH) estimation is crucial for ensuring safe and reliable operations of electric vehicles (EVs). Yet, such estimation problem remains a challenge in reality due to complex battery degradation behaviors and dynamic EV operations. This paper proposes a novel deep learning-based framework, a bilateral branched Visual Transformer with Dilated Self-Attention (Bi-ViT-DSA), for online SOH estimation. The proposed framework considers partial charging segments during incomplete charging based on two mainstream charging modes, the multi-stage fast-charging and CCCV charging. To incorporate multi-timescale battery ageing dynamics into SOH estimation, a novel bi-party input structure is developed to convey both inner-cycle and intra-cycle degradation information from raw data. The proposed Bi-ViT-DSA is developed to learn multi-timescale high-level latent features from the bi-party input in parallel for SOH estimation. A dilated self-attention (DSA) mechanism is developed to reduce redundant operations in modeling. Computational studies are conducted on datasets of batteries under different chemistries and test conditions. Results validate the feasibility and robustness of the proposed method and its superior performance over a set of state-of-the-art benchmarks. © 2023 IEEE.

Research Area(s)

  • Batteries, Computational modeling, Deep learning, Degradation, dilated self-attention, Estimation, Integrated circuit modeling, lithium-ion battery, multi-timescale degradation, partial charging curves, state of health, vision transformer, Voltage

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