  Accurate state-of-health (SOH) estimation is critical to guarantee the
safety, efficiency and reliability of battery-powered applications. Most SOH
estimation methods focus on the 0-100\% full state-of-charge (SOC) range that
has similar distributions. However, the batteries in real-world applications
usually work in the partial SOC range under shallow-cycle conditions and follow
different degradation profiles with no labeled data available, thus making SOH
estimation challenging. To estimate shallow-cycle battery SOH, a novel
unsupervised deep transfer learning method is proposed to bridge different
domains using self-attention distillation module and multi-kernel maximum mean
discrepancy technique. The proposed method automatically extracts
domain-variant features from charge curves to transfer knowledge from the
large-scale labeled full cycles to the unlabeled shallow cycles. The CALCE and
SNL battery datasets are employed to verify the effectiveness of the proposed
method to estimate the battery SOH for different SOC ranges, temperatures, and
discharge rates. The proposed method achieves a root-mean-square error within
2\% and outperforms other transfer learning methods for different SOC ranges.
When applied to batteries with different operating conditions and from
different manufacturers, the proposed method still exhibits superior SOH
estimation performance. The proposed method is the first attempt at accurately
estimating battery SOH under shallow-cycle conditions without needing a
full-cycle characteristic test.

Machine Learning;Signal Processing