# A Smart Approach to Electric Vehicle Optimization via IoT-Enabled Recommender Systems
## 1 Introduction
According to [9], the charging profiles are predicated on daily vehicle charging beginning at 17:00, 18:00, or 19:00 h. Actual EV charging loads, however, will be significantly influenced by travel routines, which differ considerably from driver to driver and from day to day. [PERSON] et al. [10] predict the EV charging patterns for various locales using GPS data from 76 automobiles. However, the study assumed that all EVs were sedan-style vehicles with comparable features. The fleets' plug-in comprises 50% mild hybrid EV charging loading, 20% fully EVs, and 30% fully hybrid for EV charging loads [11]. According to the parking period, Ref. [12] simulated the demand for EV charging by considering the spatial and temporal distribution. The author describes the investigation and performance of EV load forecasting by analyzing historical traffic and weather data and the classification of EV charging commission using the decision tree algorithm. However, this analysis only considers vehicles with 27 kWh Li-ion batteries, and it was also believed that a car could only be charged once daily. Given that a substantial portion of decisions in the power and energy domains hinge on prognostications of future requirements, the authors employed various methodologies, encompassing the Monte Carlo forecasting approach and support vector machines, to gauge the anticipated demand for EV charging [13]. A reliable evaluation metric for electrical load forecasts in V2G scheduling considers statistical features of EV charging. Multiple techniques were utilized for short-term prediction, including the akin-day method, time series analysis, regression models, expert systems, neural networks, statistical learning and fuzzy logic algorithms. The advancement, enhancement, and scholarly exploration of pertinent mathematical instruments can facilitate the formulation of more precise load forecasting techniques. Based on an accurate real-time range indicating system and traction power assessment, [PERSON] et al. [14] offer an EV driver a real-time charging recommendation. However, with the current static system, the driver cannot learn whether the CS is operational, how many vehicles are awaiting charge, or which charging points are available. The works mentioned above revealed the need for a real-time application to gather transportation data, EV battery condition, and CS status to estimate potential loads on CSs and avoid relying on aggregators [15]. This research introduces a server-based real-time forecasting application aimed at achieving two primary objectives: (i) optimizing scheduling to minimize wait times and (ii) delivering real-time charging station recommendations for EVs that combine affordability and rapid charging [16; 17]. The following are the main contributions of this suggested initiative:  
(1) The developed application for EVs needs the closest CSs with names, positions, and geographical route coordinates and reserves the slot to charge the EV before arriving at the destination. The algorithm does not need any complex data interchange. For the driver to find the CS, everything can be automatically retrieved via the Distance Vehicle Multicast Routing Protocol (DVMRP) and the internet system via openly accessible websites. (2) The suggested application is intended to be sufficiently intelligent to provide the driver with appropriate knowledge about the CSs while lowering the risk of station location with other vehicles, all with minimal data input from the driver. (3) This work proposes a flexible, real-time-based online charging scheduling scheme where each moving EV can choose, instead of depending on the selection from the aggregator. This flexible real-time-based online charging scheduling scheme considers the available efficiency, packet delivery ratio, delay and throughput. According to this perspective, our suggested system is desirable because the user solely makes the decision, and the privacy of EVs will not be revealed through any channel.