# An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques
## 1 Introduction  
An electric vehicle (EV) is a type of vehicle that is powered entirely by electricity, as opposed to vehicles that are powered by fossil fuels. EVs are becoming an increasingly popular alternative to traditional gasoline-powered vehicles due to their environmental benefits and lower operating costs. Gasoline-powered vehicles emit harmful pollutants into the air, contributing to air pollution and climate change. Meanwhile, electric vehicles do not produce any emissions during operation, making them a much more sustainable and eco-friendly option [1, 2]. They also require less maintenance and can be cheaper to operate over the long term. Electric vehicles refer to any type of vehicle that is powered by an electric motor or battery, rather than relying solely on a traditional combustion engine. This includes various types of vehicles, such as two-wheelers, three-wheelers, and four-wheelers. Despite the many benefits of reduced emissions and lower operating costs that four-wheelers provide, there are still obstacles that need to be overcome to make them a feasiblechoice for all drivers, including challenges such as range anxiety [3; 4], a lack of charging infrastructure, high upfront costs, and long charging times. Similarly, two-wheeler e-mobility (such as e-bikes and scooters) also has its advantages, but it shares the same concerns as four-wheelers, including range anxiety, which is a common concern among electric two-wheeler users who worry about running out of battery power while on the road [5]. Even though e-bikes can alleviate the riders' concerns about range anxiety by providing pedals, which can assist in conserving battery power, it is still necessary to determine the exact range. The main difference between range prediction for four-wheelers and two-wheelers is the size of the battery and the energy consumption of the vehicle where the estimation of range is primarily dependent on the battery of the vehicle. Electric four-wheelers usually have larger battery packs, which means they can travel longer distances on a single charge compared to two-wheelers [6]. As a result, range prediction for four-wheelers usually involves more complex models. On the other hand, two-wheelers have smaller battery packs and are typically used for shorter trips, such as commuting or running errands. Range prediction for electric two-wheelers is, therefore, simpler and can often be done using basic information, such as the initial battery charge, bike and engine type, and the weight of the rider. Another key difference is the impact of speed on range prediction. Four-wheelers are typically designed to travel at higher speeds, which means they consume more energy, and their range is more affected by factors such as wind resistance and drag. Two-wheelers, on the other hand, have lower maximum speeds and are less affected by these factors. This means that range prediction for two-wheelers is less sensitive to variations in speed compared to four-wheelers. Electric two-wheelers typically come with a digital display, which indicates battery voltage. However, this does not help the consumers, as they cannot be translated to the remaining range of the vehicle. The battery size and current state of charge determine how much further the rider can go. Therefore, a more precise calculation might increase the likelihood of e-bike adoption even more [4]. Some e-mobility provides range information based on assistance level required from the drive unit [7]. However, there are other factors, such as battery size and type, vehicle weight, terrain, weather conditions, and rider behavior, that play an important role in prediction of range other than the drive unit [8]. Therefore, the accuracy of the information is not sufficient for the current riders. This creates anxiety among e-mobility owners about inadequate battery charges [9]. It is possible to obtain this information using several sensors, as modern e-bikes include sensors that transmit data to the control unit, such as speed, cadence, torque, and battery temperature.