Abstract
An increase in the number of electrical vehicles has resulted in an increase in the number of electrical vehicle charging stations. As a result, the electricity load consumed by charging stations has become large enough to de-stabilize the electricity supply system. Therefore, real-time monitoring of how much electricity each charging station is consuming has become very much important. However, only limited information such as charging time is available from the operators of electric vehicle charging stations. The actual electricity consumption data is not provided in real time. Conventional methods estimate the accumulated electricity consumption of charging stations using a linear regression curve. However, an estimate of the electricity consumption for each charge is needed. In this paper, we propose an advanced electricity estimation system which predicts the energy consumption for each charge. The proposed method uses a constraint-aware non-linear regression curve, and performs additional data selection processes. The experimental results show that the proposed system achieves about 73% regression accuracy. In addition, the proposed system can display the energy consumption per hour and visualize this information on a map. This makes it possible to monitor the electricity consumption of the charging stations in real-time and by location, which helps to select appropriate locations where new vehicle charging stations need to be installed.
Highlights
Electric-driven vehicles have been attracting much attention due to their efficient reduction of carbon dioxide emission [1]
We propose a novel constraint-aware method based on a non-linear regression curve for estimating the electricity consumption per charge from the charging time without any supplementary information
The consists of a training phase which phase, generates an electricity consumption estimation model forsystem each charging station, and an operating which electricity consumption estimation model charging station, and operating phase, which estimates the electricity consumption from for theeach real-time operating data ofan charging stations based on estimates the electricity consumption from the real-time operating data of charging stations based on the learned model
Summary
Electric-driven vehicles have been attracting much attention due to their efficient reduction of carbon dioxide emission [1]. Proposed a method using a greedy algorithm and land-use information All these methods do not estimate the actual electricity consumption and consider the overload situation by EV charging stations. Cheon et al [5] proposed a regression model that estimates the energy consumption using the charging time and the number of charge cycles This method uses a linear regression curve to estimate the monthly cumulative electricity consumption rather than the electricity consunmption per charge. The electricity consumption per charge, not the cumulative electricity consumption, is important information for estimating real-time electricity load to prevent an overload situation All these methods require use of additional information to estimate energy consumption, which can cause unexpected costs and efforts.
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