A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Fault Detection of Electric Vehicle Charging Piles Based on Extreme Learning Machine Algorithm
With electric cars, large-scale development, in order to make the electric vehicles charging more convenient and efficient, public charging piles began to be used on a large scale. However, traditional fault detection methods are still used in charging piles, which makes the detection efficiency low. This paper proposes an error detection procedure of charging pile founded on ELM method. Different from the traditional charging pile fault detection model, this method constructs data for common features of the charging pile and establishes a classification prediction frame work that relies on the Extreme Learning Machine (ELM) algorithm. Experimental results evinces that the frame works accuracy is 83%, with a high efficiency, strong practicability, and is easy to popularize.
Fault Detection of Electric Vehicle Charging Piles Based on Extreme Learning Machine Algorithm
With electric cars, large-scale development, in order to make the electric vehicles charging more convenient and efficient, public charging piles began to be used on a large scale. However, traditional fault detection methods are still used in charging piles, which makes the detection efficiency low. This paper proposes an error detection procedure of charging pile founded on ELM method. Different from the traditional charging pile fault detection model, this method constructs data for common features of the charging pile and establishes a classification prediction frame work that relies on the Extreme Learning Machine (ELM) algorithm. Experimental results evinces that the frame works accuracy is 83%, with a high efficiency, strong practicability, and is easy to popularize.
Fault Detection of Electric Vehicle Charging Piles Based on Extreme Learning Machine Algorithm
Gao, Xinming (author) / Yuan, Gaoteng (author) / Zhang, Mengjiao (author)
2020-03-01
120454 byte
Conference paper
Electronic Resource
English
Assembly Line Detection Based on Dynamic Programming for Charging Piles
| Springer Verlag | 2024