Motor torque estimation and security control for electric vehicles based on parameters feature extraction

Abstract

Motor as well as its controller plays an important role in driving electric vehicle. As sole power device, it is closely related with actual torque accuracy to make sure security during EV driving. Due to complex controlling system for motor, there are some random failures of hardware and software which can bring a series of unexpected risks for EV acceleration or deceleration. A novel method based on motor parameters feature is proposed to estimate motor torque based on parameters feature extraction for three-phase of volts and currents. Additionally, Quality Factor ( QF ) and confidence coefficient are also adopted to judge whether motor estimation torque is reasonable or not and motor failure torque is limited to be output by setting fault flag in controller software. Finally, test bench is built to analyze torque accuracy compared with actual test value and verify security control strategy at failure modes, test results show that estimation torque accuracy is within ±5 Nm which is compared with actual test torque and motor system can effectively come into security state from failure state by security control strategy which is proposed in this paper.