Automotive Electronic Control Unit Ground Line Health Monitoring Method

Abstract

Electronic Control Units (ECUs) have been used in the automotive industry for decades to control one or more of the vehicle subsystems. The ECUs communicate primarily using the in-vehicle Controller Area Network (CAN) communication protocol. The recent rapid development of connected, electric, and autonomous vehicles expands the number of ECUs and complexity of the CAN network required to integrate vehicle systems and deliver the desired functionalities. This demands increased reliability of the ECUs to ensure for robust vehicle performance. One of the most common ECU failure modes is the ECU ground fault. A ground fault occurs when the ground path in the ECU circuit is corroded, which is usually developed slowly over time. Such failure usually results in various symptoms including ECU incapable of functioning and further impacts the vehicle functionalities negatively. This type of fault can be difficult to detect prior to vehicle functionality loss. It usually involves routinely testing the resistance of the ground circuit, visually inspecting the connectors and wirings, and checking the voltage drop across the ground circuit. Therefore, it is highly desirable to continuously monitor the ECU ground line health status to predict any degradation and thus prevent vehicle functionality losses.
 This paper presents a novel method to monitor the health status of ECU ground line. The method leverages measured CAN voltage data to estimate the ECU ground state of health. The CAN voltage measurements are preprocessed and fed into a real-time data buffer of predefined size. Statistical moments are calculated from the buffered data to generate health indicators, which are then combined to form a fused health indicator. The fused health indicator is used to determine the health stage of ECU ground line. The health stage is classified based on the relationship between ground line degradation level and the ECU communication loss status. The method was developed and validated using actual vehicle data.