We propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model. The proposed method uses multi-feature inputs based on observations of historical weather (wind speed, temperature, and humidity) data as multiple inputs to a Long Short-Term Memory (LSTM) model to achieve a robust prediction of charging loads. Weather conditions are significant influencers of the behavior of EV drivers and their driving patterns. These behavioral and driving patterns affect the charging patterns of the drivers. Rather than one prediction (step, model, or variables) made by conventional LSTM models, three charging load (energy demand) predictions of EVs were made depending on different multi-feature inputs. Data fusion was used to combine and optimize the different charging load prediction results. The performance of the final implemented model was evaluated by the mean absolute prediction error of the forecast. The implemented model had a prediction error of 3.29%. This prediction error was lower than initial prediction results by the LSTM model. The numerical results indicate an improvement in the performance of the EV load forecast, indicating that the proposed model could be used to optimize and improve EV load forecasts for electric vehicle charging stations to meet the energy requirements of EVs.

Recent motivation to cut greenhouse gas emissions to combat climate change has led to increasing transportation electrification. However, electric vehicle proliferation comes with a number of challenges such as battery capacities and the range anxiety of electric vehicles. In this paper, a review of the main components that affect electric vehicle adoption, which are charging infrastructure and energy resources, is presented. We discuss the categories of electric vehicle charging infrastructure, based on the location-of-charge and the charging technology. In addition, a review of the energy resources required for electric vehicles is also presented. The key features of these batteries are also discussed.