Electric vehicles (EVs) are inducing revolutionary developments to the transportation and power sectors. Their innumerable benefits are forcing nations to adopt this sustainable mode of transport. Governments are framing and implementing various green energy policies. Nonetheless, there exist several critical challenges and concerns to be resolved in order to reap the complete benefits of E-mobility. The impacts of unplanned EV charging are a major concern. Accurate EV load forecasting followed by an efficient charge scheduling system could, to a large extent, solve this problem. This work focuses on short-term EV demand forecasting using three learning frameworks, which were applied to real-time adaptive charging network (ACN) data, and performance was analyzed. Auto-regressive (AR) forecasting, support vector regression (SVR), and long short-term memory (LSTM) frameworks demonstrated good performance in EV charging demand forecasting. Among these, LSTM showed the best performance with a mean absolute error (MAE) of 4 kW and a root-mean-squared error (RMSE) of 5.9 kW.
The application of deep learning in autonomous vehicles has surged over the years with advancements in technology. This research explores the integration of deep learning algorithms into autonomous vehicles (AVs), focusing on their role in perception, decision-making, localizatio…
Battery state of charge prediction is one of the most essential state quantities of a battery management system. It is a prerequisite for the operation of a battery management system, but it becomes difficult to make an exact prediction of its state due to its characteristics, wh…
Machine learning techniques have advanced rapidly, leading to better prediction accuracy within a short computational time. Such advancement encourages various novel applications, including in the field of operations research. This study introduces a novel way to utilize regressi…
Due to developments in vehicle engineering and communication technologies, vehicular networks have become an attractive and feasible solution for the future of electric, autonomous, and connected vehicles. Electric autonomous vehicles will require more data, computing resources,…
Electric vehicles (EVs) are becoming increasingly popular, due to their beneficial environmental effects and low operating costs. However, one of the main challenges with EVs is their short battery life. This study presents a comprehensive approach for predicting the Remaining Us…
The increased demand for electricity is inevitable due to transport sector electrification. A major part of this demand is from electric vehicle (EV) charging on a large scale, which is now a growing concern for the grid power distribution system. The lack of insight into grid en…