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Accurate state-of-health (SOH) estimation is critical to guarantee thesafety, efficiency and reliability of battery-powered applications. Most SOHestimation methods focus on the 0-100\% full state-of-charge (SOC) range thathas similar distributions. However, the batteries in real-world applicationsusually work in the partial SOC range under shallow-cycle conditions and followdifferent degradation profiles with no labeled data available, thus making SOHestimation challenging. To estimate shallow-cycle battery SOH, a novelunsupervised deep transfer learning method is proposed to bridge differentdomains using self-attention distillation module and multi-kernel maximum meandiscrepancy technique. The proposed method automatically extractsdomain-variant features from charge curves to transfer knowledge from thelarge-scale labeled full cycles to the unlabeled shallow cycles. The CALCE andSNL battery datasets are employed to verify the effectiveness of the proposedmethod to estimate the battery SOH for different SOC ranges, temperatures, anddischarge rates. The proposed method achieves a root-mean-square error within2\% and outperforms other transfer learning methods for different SOC ranges.When applied to batteries with different operating conditions and fromdifferent manufacturers, the proposed method still exhibits superior SOHestimation performance. The proposed method is the first attempt at accuratelyestimating battery SOH under shallow-cycle conditions without needing afull-cycle characteristic test.

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