Abstract
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.
Abstract (translated)
准确且快速的状态健康（SOH）监测在指示锂离子电池供电便携移动设备的能源信息方面起着重要作用。为了应对这些设备工作条件的变化，迁移学习（TL）作为一种利用数据丰富的源环境知识的技术应运而生，这显著减少了目标工作条件下进行状态健康监测所需的数据训练量。然而，由于传统的基于迁移学习的状态健康监测在应用到便携移动设备时会消耗大量的计算资源，并意外地缩短了设备的工作寿命，因此这种方法在这些设备上是不可行的。 为解决上述挑战，本文提出了一种采用构造性增量迁移学习（CITL）的轻量级状态健康监测方法。首先，利用目标领域的未标记数据，提出了一个半监督迁移学习机制，通过迭代地在网络中添加节点来以建设性的方式最小化监测残差。其次，通过结构风险最小化、转移不匹配最小化和流形一致性最大化综合保证了CITL的跨域学习能力。此外，还给出了CITL的收敛分析，在理论上确保了迁移学习性能的有效性和网络紧凑性。 最后，本文通过广泛的实验验证了所提出的方法，这些实验使用了一个实际的无人机（UAV）电池数据集进行，该数据集从数十次飞行任务中收集而来。具体而言，与SS-TCA、MMD-LSTM-DA、DDAN、BO-CNN-TL和AS$^3$LSTM方法相比，在状态健康估计方面，CITL分别提高了83.73%、61.15%、28.24%、87.70%和57.34%，使用均方根误差作为评估指标。
URL
https://arxiv.org/abs/2512.08512
