Model-Based Sensor Fault Detection and Diagnosis in Closed-Loop Power Converters for Electric Vehicles
Year: 2024
Type: article
Abstract: Electric Vehicle (EV) involves electric motors equipped with closed loop powered conversion systems which use the energy from storage batteries for vehicle propulsion and rely on sensor measurements as feedback to the controller. Therefore, sensor fault detection and diagnosis are an essential process that ensures performance and safety of EVs. From the National Renewable Energy Laboratory (NREL) study it is confirmed that electrical vehicle’s fault percentages vary, however, sensor faults constitute approximately 20-30% of total faults in them. Thus, sensor fault detection is critical for effective EV operation and requires the utmost focus from EV manufacturers and operators. Model-based techniques and Kalman Filter (KF) make it possible to detect faults on sensors in closed-loop EV power converter. It involves the use of mathematical model, sensor data and KF to identify fault before it becomes a threat and guarantees proper function of the vehicle. In this article, a state-space model of a CUK converter is developed, and an LQR closed-loop control system is designed for the power converter. A sensor fault detection method was developed for the system which is then simulated and tested under sensor bias fault conditions for both open and closed-loops control for determining the difference between them. The results showed a distinctive feature extracted between these two systems in the presence of system disturbances and noise applied to the sensor readings. The second section of simulation results in a closed-loop configuration validated the mathematical equations used for sensor fault detection, demonstrating their resistance to false alarms caused by system or component faults in addition to its ability to diagnose faults.
Source: IFAC-PapersOnLine
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4401630985