Several AI-based approaches have been emerging in all segments of EV right from battery charging to network congestion management algorithms. In this concerted effort, transportation engineers, power systems experts, and AI researchers are being involved in order to bring the benefits of such solutions to the real world.
Due to the long charging times and limited range, to optimize the battery usage, various techniques using Artificial Intelligence can be introduced. The optimum EV charging unit can generate a series of decisions to maximize the range of an EV and its battery charge effectively.
In some situations, it can turn out that the most energy efficient route can be considerably longer than the shortest or fastest one. The AI powered charging units can turn out the most efficient method to optimise battery be it the shortest or the farthest route.
Presently, insufficient number of charging infrastructures is the main factor that prevents larger penetration of EVs. The drivers are lagging behind and hesitating to buy EVs as there are no proper available charging infrastructures, and similarly charging infrastructure operators do not invest while the number of EVs are not profitable.
Development of an information system model can include variables relevant to the recharging model of electric vehicles in a data-driven framework.
This model can estimate the waiting time for the selected charging point which can then be compared to the waiting times for the rest of the charging points.
Based on previous stored data, a probability of an EV driver deviating from the plan and going to another charging point can be analysed and calculated. These probabilities are used in making more accurate predictions on future waiting times.
The opening of new public charging units involves the estimation of the visitation patterns to ensure greatest possible utilization that in turn can justify the allocated resources.
Method of selecting relevant data using the stratification by clusters can significantly decrease the time required to train forecasting models with results close to those obtained.
Hence, to support decision making in this area a model can be developed using data-driven approaches with a predictive power.
In contrast to the large number of studies, there are few innovations in the context of location analysis that are tracked using data analysis methods.
A classification model using deep learning networks allows the determination of whether or not to charge the vehicle can generate an optimal recharge decision in real time without considering knowledge of the future information.
The predictive power of various machine learning models can determine the ranking of potential localities for retail stores and can be analysed to conclude mobility and geographic features that strongly improves the results.
