Optimized real-time energy prediction for EV power stations using hybrid algorithms- A review

The rapid increase of electric vehicles (EVs) has significantly impacted global energy consumption, which presents unique and new challenges for predicting energy demand for charging of these vehicles. Models that predict accurately are essential for optimizing utilization of charging stations and that also ensures grid stability. This review paper critically examines a wide variety of techniques that are used for prediction, including statistical approaches such as autoregressive integrated moving average (ARIMA) and Kalman filters, and also machine learning methods like support vector machines (SVM), Bayesian extreme Machine learning machines (BEML), and it also analyses advanced deep learning models such as long short-term memory (LSTM) networks and hybrid frameworks. Key factors that influence EV charging demand are vehicle conditions, state-of-charge (SOC), driving patterns, distance-to-destination and geographical characteristics—are studied. After all these advancements, there are still challenges that need to be focused, like data heterogeneity, computational complexity, and scalability. This study summarizes existing works and highlights the effectiveness of novel integrated models, including empirical mode decomposition (EMD), optimization algorithms, and neural networks. These kinds of studies aim to guide the development of robust, scalable, and prediction systems that are real-time and support energy distributors to manage EV charging infrastructures effectively.