# An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques
## 3 Materials and Methods
### Data Collection  
An application named \"Ddanigo LLC. Navigation app\" has been developed under the company Ddanigo LLC, 4701 Patrick Henry Dr #23, Santa Clara, CA 95054, USA [26] for collecting data and integrating machine learning techniques in the background, inspired by [8; 22]. The mobile application uses flutter to ensure cross-platform availability for both Android and iOS devices. In addition to the application, we have also compiled a comprehensive dataset containing technical information for two-wheelers worldwide. Our database contains information for 732 two-wheelers, each of which has been assigned a unique ID (named \"E_ID\" in storage) for easy retrieval and analysis of technical information. This dataset encompasses a range of attributes, including the two-wheeler name, model, year, weight, voltage (V), battery capacity in watt-hours (Wh), and maximum mileage in kilometers (Km). By analyzing this diverse and extensive dataset, we aim to gain valuable insights into the performance and efficiency of various two-wheeler models. This dataset was prepared to support the user registration process in the application. When a user registers in the application, they are required to provide details about their two-wheeler, which are then matched against the information in our database. To simplify this process for users, we only require basic information, such as the name, model, and manufacturing year, which can be easily found on the manufacturer's website. By matching the user's two-wheeler details with the technical information in our dataset, the application's database creates another unique ID (named \"UE_ID\" in storage) that combines both the user and two-wheeler information. This ensures that users can easily access accurate technical specifications for their two-wheeler without needing to worry about the details themselves. However, if a user's two-wheeler is not found in the dataset, they can still register their vehicle by providing basic information. The application will then generate a unique ID, even if technical specifications are not available. Once the two-wheeler has been set up, the application can provide navigation services. While providing navigation services, the application can collect data in the background, which are then saved to cloud storage after each individual trip. The data we have collected during each trip can be categorized into four categories: two-wheeler data, environmental data, route data, and rider behavior data. The choice to use data for range prediction is well founded, as these data categories are directly related to the factors that can influence the range of a two-wheeler. As we have discussed in Section 2, the authors [8] explored the impact of similar factors such as two-wheeler, environmental, route, and bicyclist effort data, where bicyclist effort was measured in terms of power. On the other hand, the study [22] also focused on comparable factors but they used different aspects of bicyclist performance, which is average speed. While measuring power accurately often requires additional sensors and hardware, the goal in this case is to provide a solution that does not rely on extra equipment. Instead, our focus was to utilize easily accessible information that can be collected through a smartphone. By concentrating on distance, time, and speed as rider behavior metrics, we can analyze and understand performance without the need for specialized sensors. The experimentation was carried out only with an android device. The application requires internet connectivity and GPS to perform data collection. The routes were se \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Details** \\ \hline Battery Voltage & 36 V \\ Battery Capacity & 6 Ah \\ Motor Output & 36 V 300 W (Rated Power) \\ Weight & 13.5 Kg \\ Maximum Range & 20 km \\ Maximum Speed & 25 km/h \\ Wheel Size & 8 inches \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Technical details of Road Gear CT electric scooter.