Research on Fault Diagnosis of Multi-Mode Electromechanical Compound Transmission System for Hybrid Electric Vehicles Based on Kernel Density Estimation

To effectively monitor operational status and detect faults during mode transitions in hybrid electric vehicles, This study examines the multimode electromechanical compound transmission system (MM-EMCT HEV), a specific model of power-split hybrid electric vehicles. It identifies key faults based on their occurrence frequency and the feasibility of simulating them in MM-EMCT HEVs. A fault diagnosis framework is developed utilizing analytical redundancy relations (ARRs), which incorporates strategies for generating residuals across sensors and actuators, along with evaluation methods using kernel density estimation. The detectability and isolatability of the MM-EMCT are analyzed in detail. Simulations and verifications of the residual responses under common faults are performed using Matlab/Simulink and Simscape. The findings indicate that residual evaluation based on kernel density estimation effectively detects and isolates faults in multimode electromechanical compound transmission systems.