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Dual time-scale state-coupled co-estimation of SOC, SOH and RUL for lithium-ion batteries via Deep Inter and Intra-Cycle Attention Network

Ningbo Cai, Yuwen Qin

2022enlithium-ion batteryRULSOHSOCattention mechanism

Abstract

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Accurate co-estimations of battery states, such as state-of-charge (SOC), state-of-health (SOH), and remaining useful life (RUL), are crucial to the battery management systems to assure safe and reliable management. Although the external properties of the battery change with the aging degree, batteries’ degradation mechanism shares similar evolving patterns. Since batteries are complicated chemical systems, these states are highly coupled with intricate electrochemical processes. A state-coupled co-estimation method named Deep Inter and Intra-Cycle Attention Network (DIICAN) is proposed in this paper to estimate SOC, SOH, and RUL, which organizes battery measurement data into the intra-cycle and inter-cycle timescales. And to extract degradation-related features automatically and adapt to practical working conditions, the convolutional neural network is applied. The state degradation attention unit is utilized to extract the battery state evolution pattern and evaluate the battery degradation degree.

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Cite This Work

@article{5765f03d-51a5-4702-97ed-e34dedc208b3,
  title={Dual time-scale state-coupled co-estimation of SOC, SOH and RUL for lithium-ion batteries via Deep Inter and Intra-Cycle Attention Network},
  author={Ningbo Cai and Yuwen Qin},
  year={2022},
  language={en}
}
TY  - JOUR
TI  - Dual time-scale state-coupled co-estimation of SOC, SOH and RUL for lithium-ion batteries via Deep Inter and Intra-Cycle Attention Network
AU  - Ningbo Cai
AU  - Yuwen Qin
PY  - 2022
LA  - en
ER  -

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