In the controller development process, it is required to verify whether control functions operate normally, without fault or anomaly. Many control functions with complex system structures require various actual tests and test data analysis for verification. This paper presents an anomaly detection algorithm to verify control functions and to more efficiently analyze test data of a hybrid control unit (HCU) of a hybrid electric vehicle (HEV). The anomaly detection algorithm automatically detects anomaly of control functions from the test data, instead of manual labor by engineers. The examined target HCU control functions in this paper are the engine clutch engagement/disengagement control function and the engine start cooperative control function, which are crucial functions of transmission mounted electric drive (TMED) HEVs. A data-driven approach using a one-class support vector machine (SVM) is used to make it easy to apply various control functions. Actual vehicle test data is examined by the algorithm to verify the control functions at an actual vehicle level. The developed anomaly detection algorithm demonstrates feasibility and effectiveness of the proposed algorithm in detecting not only prior-known anomalies but also prior-unknown anomalies. Because other vehicle control functions have similar characteristics with the target functions of this paper, this algorithm is expected to be successfully applied to other HCU control functions.

中文翻译：

---

使用一类 SVM 对混合动力电动汽车进行基于事件的异常检测


在控制器开发过程中，需要验证控制功能是否正常运行，无故障或异常。许多系统结构复杂的控制功能需要进行各种实际测试和测试数据分析来验证。本文提出了一种异常检测算法，用于验证控制功能并更有效地分析混合动力电动汽车 (HEV) 混合控制单元 (HCU) 的测试数据。异常检测算法根据测试数据自动检测控制功能的异常，无需工程师手工操作。本文研究的 HCU 目标控制功能是发动机离合器接合/分离控制功能和发动机启动协调控制功能，它们是变速器安装式电驱动 (TMED) HEV 的关键功能。使用一类支持向量机 (SVM) 的数据驱动方法可以轻松应用各种控制功能。通过算法检查实际车辆测试数据，以验证实际车辆级别的控制功能。所开发的异常检测算法证明了所提出的算法不仅可以检测先验已知的异常，还可以检测先验未知的异常的可行性和有效性。由于其他车辆控制功能与本文的目标功能具有相似的特性，因此该算法有望成功应用于其他HCU控制功能。