碳化硅（SiC）功率模块的物理信息型状态监测
Physics-Informed Condition Monitoring of SiC Power Modules
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中文总结 AI 辅助
本文针对采用烧结封装的SiC MOSFET模块，提出融合物理信息型特征、梯度惩罚单调性约束与重尾输出分布的状态监测框架，在英飞凌工业功率循环数据集上使平均绝对误差降约70%且适配嵌入式部署。
中文摘要 AI 辅助
碳化硅（SiC）功率模块正越来越多地应用于汽车牵引逆变器中，状态监测对于防止其服役期间发生故障至关重要。尽管依据AQG 324标准进行了大量定型试验，但目前尚无统一的现场健康状态估计方法：失效物理寿命模型缺乏实时适用性，纯数据驱动架构需要大量标注数据集且泛化能力差，而物理信息型框架对嵌入式部署而言要求过高。本文研究采用烧结封装的SiC MOSFET模块，该封装可抑制焊料退化，产生的老化行为与此前研究的器件不同。与基于焊料的模块呈现的平滑准指数漂移不同，其正向压降$V_{DS}$呈现多段分布，且键合线 lift-off（脱落）事件会引入突然的非单调扰动。本文提出的状态监测框架包含三个核心要素：其一，物理信息型特征将原始传感器信号替换为基于结温波动、平均结温及Miner规则累加器的累积损伤指标，以可解释形式编码退化历史；其二，通过梯度惩罚正则化施加的单调性约束，将预期退化方向作为物理引导先验嵌入模型；其三，采用重尾输出分布替代点估计，得到对键合线脱落引入的分布外方差具有鲁棒性的校准不确定性。在英飞凌科技提供的工业功率循环数据集上，本文在严格交叉验证协议下对比了多种神经架构。完整配置相较于纯数据驱动基线将平均绝对误差降低约70%，且在所有折中保持稳定，同时足够轻量可用于嵌入式部署。
英文摘要
Silicon carbide (SiC) power modules are increasingly deployed in automotive traction inverters, where condition monitoring is essential to prevent in-service failures. Despite extensive qualification under AQG 324, no consolidated approach exists for in-field health state estimation: physics-of-failure lifetime models lack real-time applicability, purely data-driven architectures require large labeled datasets and generalize poorly, and physics-informed frameworks remain too demanding for embedded deployment. We address SiC MOSFET modules assembled with sintered packaging, which suppresses solder degradation and produces aging behavior distinct from previously studied devices. Instead of the smooth quasi-exponential drift of solder-based modules, the forward voltage drop $V_{DS}$ exhibits multi-regime profiles, with wirebond liftoff events introducing abrupt, non-monotonic perturbations. We propose a condition monitoring framework combining three elements. First, physics-informed features replace raw sensor signals with cumulative damage indicators derived from junction temperature swing, mean junction temperature and a Miner rule accumulator, encoding degradation history in an interpretable form. Second, a monotonicity constraint enforced by gradient penalty regularization embeds the expected degradation direction as a physics-guided prior. Third, a heavy-tailed output distribution replaces the point estimate, giving calibrated uncertainty robust to the out-of-distribution variance introduced by liftoff. On an industrial power cycling dataset from Infineon Technologies, several neural architectures are compared under a strict cross-validation protocol. The full configuration reduces mean absolute error by approximately 70% over purely data-driven baselines and stays stable across all folds, while remaining lightweight enough for embedded deployment.
发表机构
- University of Padova（帕多瓦大学）
- Infineon Technologies Dresden GmbH & Co. KG（英飞凌德累斯顿有限合伙公司）
- Infineon Technologies AG（英飞凌股份公司）
- Newtwen S.r.l.（纽特文有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。
