Research on multi-sensor-based online inspection technology for electric vehicle charging pile transfer type equipment

Abstract

This paper performs data fusion based on a multi-sensor fusion algorithm, which measures the sensors and calculates the distance between the data to obtain the measurement weights when participating in the fusion. The sensor observation model is created using an extended Kalman filter, and the nonlinear state transition function is linearized using the covariance matrix. Accurately estimating sensor inter-cluster data is necessary to achieve the scalability of online detection technology for charging piles. The results show that the disconnection time of the contactor of the charging pile transfer type equipment is 1.153s after the simulated charging pile output over-voltage in the disconnection time detection. The online detection efficiency can be improved by using multiple sensors, the method analysis can be intuitive, and the charging service capability of the electric vehicle charging pile can be effectively improved.

Keywords

• multi-sensor fusion
• electric vehicle charging
• kalman filtering
• covariance matrix
• online monitoring
• 78-02