An Optimized Bi-LSTM Machine Learning Model for Predicting Congestion at Electric Vehicle Charging Stations

Abstract

The proliferation of electric vehicles (EVs) has intensified the need for efficient and reliable charging infrastructure. This study introduces a bidirectional long short-term memory (Bi LSTM)-based model designed to predict and manage congestion at EV charging stations. Leveraging the advanced capabilities of Bi LSTM networks in handling sequential data, our model analyzes historical and real-time data to forecast congestion levels. The bidirectional nature of the LSTM allows for a comprehensive analysis of the data, capturing dependencies from both past and future contexts. The proposed model aims to provide real-time intimation to both users and operators, enhancing decision-making processes and optimizing the utilization of charging resources. By offering accurate predictions of congestion, the Bi LSTM-based model facilitates strategic planning for station deployment and user navigation, ultimately improving the overall efficiency of the charging infrastructure. Experimental results demonstrate the model's efficacy in accurately predicting congestion, significantly reducing wait times, and improving user satisfaction. This research underscores the potential of advanced machine learning techniques, particularly Bi LSTM networks, in addressing the dynamic challenges of EV charging station management. The implementation of such predictive models is a crucial step toward the development of a smart, efficient, and user-centric EV charging ecosystem.