# An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques
## 2 Related Works  
There have been numerous studies conducted on range prediction for electric vehicles; most of these studies have focused on three- or four-wheeler electric vehicles. However, there are only a few studies that have specifically addressed range prediction for electric bikes or two-wheelers. This highlights the need for further research in this area to improve the accuracy of range prediction for electric two-wheelers. Range prediction is a standard feature when it comes to four-wheeled electric vehicles (EV) to inform the drivers when to charge the vehicle. To overcome the range prediction problem in the case of EV, physical models using real-time data and artificial intelligence models both trained on previous vehicle data have been investigated to estimate the range of EV where physical models are based on their ability to provide an estimate of energy consumption by modeling either the battery pack or the vehicle itself (see, e.g., [13; 14; 15; 16; 17; 18; 19; 20; 21]). However, when it comes to the two-wheeeler e-mobility especially, there are very few works that have been conducted [8; 10; 11; 12; 22; 23; 24]. We present a brief overview of the related research we have compiled in Table 1 where we mentioned details of the e-mobility type and approach of each study. There are some studies that focused on battery power consumption rather than range estimation. We know the main power source of a two-wheeeler is the battery, which is one of the factors to determines the range. In [22], the authors studied battery power and developed a mathematical model for power consumption based on road slope, friction, air resistance, rolling resistance, user information, and engine type, along with bike characteristics. An application was also developed to give input parameters in the mathematical model and to check each segment's battery consumption in a planned route. Two e-bikes were prepared to test their model and found that the prediction forecast is closer to the actual battery consumption. We found another similar study about battery consumption [10] where artificial neural networks were used to calculate rough estimates of travel time and battery consumption along a given route and then presented through visualization to the user. Evolutionary algorithms used these approximations as part of their optimization processes. The outcomes for trips taken with and without the optimization system have been compared. However, mean absolute error was found to be around 9.7% without optimization and a much better result was achieved after optimization. Moreover, the result was not the same for two similar batteries. We understand that battery consumption is directly related to range prediction. However, our focus is on estimating the maximum distance that can be covered by the vehicle before the battery runs out of power. In [11], they ensured the safe and effective operation of e-mobility systems by better understanding how batteries react to a wide range of conditions through the development of a battery simulation model. A two-wheeeler was prepared for on-road test in Italy to measure the battery information during each test ride. Then, the test data were examined through their models, which are battery model, longitudinal dynamic model, and global model, where all the models were studied through Matlab Simulink. Battery models have been prepared using battery information such as voltage, current, total energy, discharge current, weight, and so on. In the dynamic model, it is