Accurate and rapid state-of-health (SOH) monitoring plays an important role in indicating energy information for lithium-ion battery-powered portable mobile devices. To confront their variable working conditions, transfer learning (TL) emerges as a promising technique for leveraging knowledge from data-rich source working conditions, significantly reducing the training data required for SOH monitoring from target working conditions. However, traditional TL-based SOH monitoring is infeasible when applied in portable mobile devices since substantial computational resources are consumed during the TL stage and unexpectedly reduce the working endurance. To address these challenges, this paper proposes a lightweight TL-based SOH monitoring approach with constructive incremental transfer learning (CITL). First, taking advantage of the unlabeled data in the target domain, a semi-supervised TL mechanism is proposed to minimize the monitoring residual in a constructive way, through iteratively adding network nodes in the CITL. Second, the cross-domain learning ability of node parameters for CITL is comprehensively guaranteed through structural risk minimization, transfer mismatching minimization, and manifold consistency maximization. Moreover, the convergence analysis of the CITL is given, theoretically guaranteeing the efficacy of TL performance and network compactness. Finally, the proposed approach is verified through extensive experiments with a realistic unmanned air vehicles (UAV) battery dataset collected from dozens of flight missions. Specifically, the CITL outperforms SS-TCA, MMD-LSTM-DA, DDAN, BO-CNN-TL, and AS$^3$LSTM, in SOH estimation by 83.73%, 61.15%, 28.24%, 87.70%, and 57.34%, respectively, as evaluated using the index root mean square error.

翻译：准确且快速的健康状态（SOH）监测对于指示锂离子电池驱动的便携式移动设备的能量信息具有重要作用。为应对其多变的工作条件，迁移学习（TL）作为一种有前景的技术，能够利用数据丰富的源工作条件下的知识，显著减少目标工作条件下SOH监测所需的训练数据。然而，传统的基于迁移学习的SOH监测在应用于便携式移动设备时并不可行，因为迁移学习阶段消耗大量计算资源，意外降低了设备的工作续航能力。为应对这些挑战，本文提出了一种基于轻量级迁移学习的SOH监测方法，采用构造性增量迁移学习（CITL）。首先，利用目标域中的未标记数据，提出了一种半监督迁移学习机制，通过CITL中迭代增加网络节点，以构造性方式最小化监测残差。其次，通过结构风险最小化、迁移失配最小化和流形一致性最大化，全面保障了CITL节点参数的跨域学习能力。此外，本文给出了CITL的收敛性分析，从理论上保证了迁移学习性能和网络紧凑性的有效性。最后，通过基于数十次飞行任务收集的真实无人机（UAV）电池数据集进行广泛实验，验证了所提方法的有效性。具体而言，在SOH估计中，CITL在均方根误差指标评估下分别优于SS-TCA、MMD-LSTM-DA、DDAN、BO-CNN-TL和AS$^3$LSTM方法，性能提升分别为83.73%、61.15%、28.24%、87.70%和57.34%。