Fault Diagnosis in the Automotive Electric Power Generation and Storage System (EPGS)

In this paper, we present an initial study to develop fault detection and isolation techniques for the vehicle systems that are controlled by a network of electronic control units (ECUs). The root causes of the faults include hardware components such as actuators, software within the controllers (ECUs), or the interactions between hardware and software, i.e., between controllers and plants. These faults, originating from various interactions and especially between hardware and software, are particularly challenging to address. Our basic strategy is to divide the fault universe of the cyber-physical system in a hierarchical manner, and monitor the critical variables/signals that have impact at different levels of interactions. Diagnostic matrix is established to represent the relationship between the faults and the test outcomes (also known as fault signatures). A factorial hidden Markov model-based inference algorithm, termed dynamic multiple fault diagnosis, is used to infer the root causes based on the observed test outcomes. The proposed diagnostic strategy is validated on an electrical power generation and storage system controlled by two ECUs in an environment with CANoe/MATLAB co-simulation. Eleven faults are injected with the failures originating in actuator hardware, sensor, controller hardware, and software components (sensor faults are not considered in this paper). The simulation results show that the proposed diagnostic strategy is effective in addressing the interaction-caused faults.