A Self-attention Knowledge Domain Adaptation Network for Commercial Lithium-ion Batteries State-of-health Estimation under Shallow Cycles

no code implementations • 11 Apr 2023 • Xin Chen, Yuwen Qin, Weidong Zhao, Qiming Yang, Ningbo Cai, Kai Wu

Accurate state-of-health (SOH) estimation is critical to guarantee the safety, efficiency and reliability of battery-powered applications.

Domain Adaptation Transfer Learning

DIICAN: Dual Time-scale State-Coupled Co-estimation of SOC, SOH and RUL for Lithium-Ion Batteries

no code implementations • 20 Oct 2022 • Ningbo Cai, Yuwen Qin, Xin Chen, Kai Wu

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 time scales.

Management

Remaining Useful Life Prediction Using Temporal Deep Degradation Network for Complex Machinery with Attention-based Feature Extraction

no code implementations • 21 Feb 2022 • Yuwen Qin, Ningbo Cai, Chen Gao, Yadong Zhang, Yonghong Cheng, Xin Chen

The degradation-related features extracted from the sensor streaming data with neural networks can dramatically improve the accuracy of the RUL prediction.