# An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques
## 4 Results and Discussion
The chart demonstrates how closely the predicted distances match the actual distances, and to identify any discrepancies or outliers that may indicate errors or limitations in the model. By comparing the two lines, we can clearly demonstrate the effectiveness of the machine learning model in predicting the distance travelled by the electric scooter. Figure 7 illustrates the comparison as a chart to obtain a better idea of how well our study has performed compared to others. Since our study focuses on predicting the range, we have only compared with the studies that have similar focus. The MAE is a statistical measure that indicates the average magnitude of errors in the predictions made by a model, with lower values indicating better performance. There are four studies being compared, denoted by [23; 24; 35], and our study. The MAE of the [23] study is 0.49, which means that the predictions made by that model had an average error of 0.49. Similarly, the MAE of the [24] study is 0.19 and the MAE of the [35] study is 0.28. Our study has an MAE of 0.13, which is the lowest value among all the studies compared. This suggests that the approach we took and the machine learning model used in our study have performed better than the models used in the other studies in terms of predicting the remaining distance of electric two-wheelers. The Table 6 describes the parameters used in different studies for predicting the range of electric scooters. Study [23] used three different parameters, including battery and motor, scooter, and user. Study [24] used similar parameters, such as battery and motor, scooter, and user, while another study [35] only used battery parameters. In contrast, our study used three parameters, including battery, user, and road, which are state of charge, battery consumption, elevation gain, acceleration, dec  
Figure 6: Comparison chart of actual and predicted distance on validation set. distance. By using these parameters, our study improved the accuracy of range prediction for electric two-wheelers. Figure 8 shows a comparison of the number of technical and nontechnical parameters used in different studies, including our one. The x-axis represents the number of studies, while the y-axis represents the number of technical and nontechnical parameters used in each study. The bars represent the total number of parameters used in each study, with green bars representing nontechnical parameters and red bars representing technical parameters. Furthermore, the parameters used in such predictions are more technical and will be very hard for the riders to provide. Nonetheless, our models have predicted quite accurately without having to rely on more technical attributes. The result shows us that we can narrow down the remaining distance to 130 m with less data and more toward user behavior. There is high potential using such a model for further studies. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Parameter Type** & **Parameter Details** \\ \hline \multirow{4}{*}{[23]} & Battery and Motor & Motor nominal input voltage, nominal power, nominal current, nominal battery \\  & & output voltage, capacity, max continuous discharge, max charge current \\  & Scooter & Linear velocity, weight, rolling resistance, dynamic resistance, drag forces \\  & User & Weight, average speed, maximum speed, maximum acceleration \\  & Battery and Motor & Motor power, motor torque, motor revolution speed (Basic and Max) \\  & Scooter & Weight, coefficient of drag, frontal area, rolling resistance, velocity, powertrain \\  & & efficiency, wheel diameter, accessories load, wheel radius \\  & User & Speed \\  & Battery & Discharging current, battery terminal voltage, battery capacity, time average voltage, battery temperature, scooter speed \\  & Battery & State of charge, battery consumption (initial battery level–final battery level) \\ Our study & Road & Elevation gain \\  & User & Acceleration, deceleration, stop time, distance \\ \hline \hline \end{tabular}
\end{table}