# A Smart Approach to Electric Vehicle Optimization via IoT-Enabled Recommender Systems
## 2 Literature Review  
[PERSON] et al. [18] have developed a mobile Android application that conveniently displays the nearest charging stations (CSs) in proximity to electric vehicles (EVs). To facilitate slot reservations, the application employs intelligent features to recognize real-time CS status, including slot availability and pricing, while also providing a list of available CSs and an estimated driving range. Similarly, the author investigated State of Charge (SOC) estimation and CS recommendations. Their proposed technique alerts EV drivers when the safe SOC limit is reached and provides accurate estimates of charging duration, enhancing the overall charging experience. [PERSON] et al. [19] contribute to the field with another mobile Android application that displays CS locations physically closest to the EV. While offering a list of available CSs and a driving range estimate, this application requires intelligence to dynamically assess real-time CS status, such as slot availability and pricing, for effective slot booking. [PERSON] et al. [20] provide valuable insights into designing and implementing a Dual Active Bridge-based battery charger tailored for PHEVs, focusing on mitigating low-frequency ripple in the charging current. Its findings contribute to advancing charging infrastructure for electric vehicles, addressing critical challenges in efficiency, reliability, and performance. To improve the performance of DAB converters with phase-shift control, [PERSON] et al. [21] offer insightful information that advances power electronics technology in various industrial and automotive applications. [PERSON] et al. [22] emphasize the importance of automated communication infrastructure, such as the internet or wireless networks, for a charge controller to receive EV user information. This information allows the charge controller to manage EV charging based on real-time data. Every instance of EV plug-in or out, as suggested by [23], necessitates notifying the aggregator of available SOC and anticipated departure time for optimal charging management. Ref. [24] achieves flexible EV charging by exchanging information with three distinct operators: charging service provider (CSP), retailer (RET), and distribution system operator (DSO). In Ref. [25], hardware implementation of a plug-in EV wireless charging manager is described as connecting the three stakeholders. However, a detailed description of the communication system between EVs, the store, and the DSO is needed. The paper [26] presents a novel strategy to schedule the charging points. The goal is to determine the convenient CS for a given EV through a Vehicular Ad-hoc Network (VANET) model. In this model, the CSs are determined and prioritized using four phases,such as driving, charge planning, charging scheduling, and battery charging. Charging scheduling was designed using a new optimization strategy, exponential Harris Hawks optimization (Exponential HHO) algorithm. The proposed Exponential HHO was validated using simulation and the performance was improved with maximum remaining energy of 52.709 Whr, minimal distance of 27.256 km, and a maximum average waiting time of 0.352 min in comparison with existing methods. The authors of [27] provide a comprehensive approach to forecast the mid-and long-term charging load of electric vehicles (EVs), with a focus on the Chinese city of Shenzhen. The methodology integrates several strategies to precisely forecast future electric vehicle charging requirements. Gathering data on EV usage patterns, including travel distances, charging routines, and parking durations, is the initial step in the process, which involves intensive data collection and preprocessing. Then, preprocessing is carried out on the information in order to ensure consistency and standardization. The author has developed a systematic methodology to forecast the future EV charging loads for China's next five-year plan (2020-2025).