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.
In this paper, the battery energy storage technology is applied to the traditional EV (electric vehicle) charging piles to build a new EV charging pile with integrated charging, discharging, and storage; Multisim software is used to build an EV charging model in order to simulate the charge control guidance module.
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.
In this article, a real-time fault prediction method combining cost-sensitive logistic regression (CS-LR) and cost-sensitive support vector machine classification (CS-SVM) is proposed. CS-LR is first used to classify the fault data of smart charging piles, then the CS-SVM is adopted to predict the faults based on the classified data.
Abstract: 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.
The data collected by the charging pile mainly include the ambient temperature and humidity, GPS information of the location of the charging pile, charging voltage and current, user information, vehicle battery information, and driving conditions . The network layer is the Internet, the mobile Internet, and the Internet of Things.