# A Smart Approach to Electric Vehicle Optimization via IoT-Enabled Recommender Systems
## 1 Introduction  
Recently, countries worldwide have enacted regulatory measures to tackle challenges within the transportation industry, including the growing demand for fossil fuels and urban pollution concerns. Eco-friendly EVs have drawn a lot of interest due to worries about the availability of fossil fuels, the environment, and emission restrictions. Numerous nations have set forward significant rules to facilitate the growth and adoption of EVs [1]. For instance, some cities in North America are undergoing a rapid transition from conventional vehicles to hybrid, plug-in vehicles and, subsequently, to EVs. EVs provide many advantages in terms of price and convenience. An electric vehicle (EV) does not require any maintenance, such as the expense of lubrication, the exhaust system, starter battery, or cooling fluids, as opposed to an internal combustion engine (ICE) car [2]. EVs' driving range is also expanding more quickly than in the past. With a 16-kWh rated capacity, the small Mahindra Reva car can travel 80 km on a single charge. Tesla Model X (SUV) vehicles with a 94-kWh battery pack have a range of 400-450 km. Figure 1 depicts a typical electric vehicle charging station and Figure 2 shows a block diagram of an electric vehicle charging station that includes a rectifier for converting AC to DC, a DC/DC converter is connected to boost the voltage according to the charging station's capacity to enable the effective charging of the EV's. For further safety, the system also incorporates a protection control mechanism in the vehicle and charge controller in the charging station. The size of their batteries primarily governs EVs' driving ranges, but engine complexity also plays an important role. Because recent research has concentrated on extending the driving range of EVs, battery monitoring and management technologies are necessary. These offer a thorough random of the Li-ion batteries found in electric vehicles. For high-capacity batteries, which call for a quick charging system, the fundamental operation ideas, assembly, and performance of several types of Li batteries are addressed and recommended. Rapid CSs can charge a battery from a 20% state of charge (SOC) to approximately 80% in 30 min, according to [3]. On the other hand, level II and level I require an extended charging process. A public CS is a preferable option for charging for EV owners who do not have access to their chargers. EVs and hybrid electric vehicles (HEVs) [4] have assessed the ideal charger mode, ensuring efficiency and the quickest charging time. SOC is one of the crucial factors that makes it possible for the battery to be charged appropriately and discharged, extending battery life. This word refers to the battery's rated capacity ratio and balancing capabilities. SOC, hence, aids in battery management. Various SOC estimating methods are provided in [5]. Three categories exist for new communication systems in the automobile industry: vehicle-to-driver (V2D), vehicle-to-vehicle (V2V), ref. [6] and vehicle-to-infrastructure (V2I). Governments have made significant investments, so communication technologies like V2V and V2I have undergone extensive research. These  
Figure 1: A typical electric vehicle charging station. Figure 2: Block diagram of an electric vehicle charging station. communication technologies are primarily intended to lessen traffic congestion, increase road travel safety, and prevent vehicle collisions. Still, only some studies support vehicle-to-device systems for EVs [7]; further studies in this field are required. Intentionally assisting the EV user and CS with effective power management scheduling is data communication. The closest CS receives the estimated SOC information and allocates the slot to the grid's power demand [8]. EV load forecasting is crucial for utility companies to run and manage effectively.