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.

翻译：准确的健康状态（SOH）估计对于保障电池供电应用的安全性、效率和可靠性至关重要。现有的大多数SOH估计方法都聚焦于分布相似的0-100%全荷电状态（SOC）区间。然而，实际应用中的电池通常在浅循环工况下工作于部分SOC区间，其退化特征不同且缺乏标记数据，这使得SOH估计充满挑战。为估算浅循环电池的SOH，本文提出了一种新颖的无监督深度迁移学习方法，通过自注意力蒸馏模块和多核最大均值差异技术来桥接不同域。所提方法从充电曲线中自动提取域变异特征，以将大规模标记全循环域的知识迁移至未标记的浅循环域。利用CALCE和SNL电池数据集验证了该方法针对不同SOC区间、温度及放电倍率下电池SOH估算的有效性。该方法均方根误差小于2%，且在多个SOC区间上优于其他迁移学习方法。当应用于不同工况及不同厂商的电池时，该方法仍展现出卓越的SOH估算性能。本工作是首次无需全循环特性测试即可实现浅循环工况下电池SOH精确估算的尝试。