轻量化迁移学习提升无人机电池健康状态监测精度与效率
A Lightweight Transfer Learning-Based State-of-Health Monitoring with Application to Lithium-ion Batteries in Autonomous Air Vehicles
- 构建增量式迁移学习框架，利用目标域无标签数据逐步优化网络结构
- 在真实飞行任务数据上实现比现有方法最高87.7%的误差降低
- 适合资源受限的便携设备，兼顾模型紧凑性与跨域适应能力
准确快速的电池健康状态（SOH）监测对锂离子电池供电的便携移动设备至关重要。针对工作条件多变的问题，迁移学习（TL）可借助数据丰富的源域知识，显著减少目标域监测所需的训练数据。然而，传统基于迁移学习的SOH监测因需大量计算资源，在便携设备中不可行，反而降低续航。本文提出一种轻量化迁移学习方法——构造性增量迁移学习（CITL）。首先，利用目标域无标签数据，通过迭代增加网络节点的方式，以构造性方式最小化监测残差。其次，通过结构风险最小化、迁移不匹配最小化和流形一致性最大化，全面保障节点参数的跨域学习能力。此外，给出了CITL的收敛性分析，理论上保证了迁移性能与网络紧凑性。最后，通过数十次飞行任务采集的真实自主飞行器（AAV）电池数据集进行了广泛实验验证。结果表明，相比SS-TCA、MMD-LSTM-DA、DDAN、BO-CNN-TL和AS$^3$LSTM，CITL在根均方误差指标下分别提升了83.73%、61.15%、28.24%、87.70%和57.34%。
原文摘要 · Abstract (English)
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 autonomous air vehicles (AAV) 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.
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