Insulation Detection of Electric Vehicle Using Least Mean Square Algorithm

Advancements in electric vehicle (EV) technologies have significantly increased the demand for large, high-voltage e-mobility systems, which require the compact integration of multiple components due to space constraints. Among these components, the battery is one of the most critical and expensive parts of an electric vehicle. Ensuring battery insulation, health monitoring, and fault diagnosis is essential to guarantee the safe operation of electric vehicles. To prevent electrical hazards, it is crucial to detect insulation failures quickly. The Least Mean Squares (LMS) algorithm is widely used for this purpose due to its adaptability, optimality, low computational complexity, and reliable fault recovery capabilities. This study demonstrates the effective application of the LMS algorithm for rapid insulation failure detection in e-mobility systems. The algorithm is implemented using MATLAB and Field-Programmable Gate Array (FPGA) platforms. FPGAs are highly effective for rapid detection with minimal errors, and demonstrated their performance through hardware testing. The proposed FPGA-based LMS technique is evaluated under various resistive and motor overload conditions, showcasing its effectiveness in enhancing the safety and reliability of electric vehicles.