A novel method for electric vehicle insulation detection based on the extended Kalman filter algorithm.

Addressing the critical need for enhanced safety in the burgeoning electric vehicle market, this study presents a novel insulation detection method based on the Extended Kalman Filter (EKF) algorithm. To resolve the conflict between the response speed and detection accuracy of the insulation detection system caused by Y-capacitors, the Levenberg–Marquardt (L-M) algorithm is employed to effectively estimate the parameters of the feedback voltage model developed for an unbalanced electrical bridge. The state equation of the insulation testing system has been constructed, and the EKF algorithm is applied innovatively to monitor insulation resistance and Y-capacitance, demonstrating superior anti-interference capabilities. Simulation experiments have underscored the significant contributions of the L-M algorithm in expanding the detection scope of the system. Bench tests confirmed the ability of the approach to monitor changes in insulation resistance and Y-capacitance rapidly and accurately. Under normal conditions, the maximum relative error for insulation resistance measurement is 1.56%, with a response time of 1.5 s. • Identifying insulation system parameters via Levenberg-Marquardt algorithm. • Use extended Kalman filter for insulation resistance monitoring. • Simulations and experiments validate proposed approach efficacy. [ABSTRACT FROM AUTHOR]