# An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques
## 5 Conclusions  
This paper addresses the challenge of predicting the range of electric two-wheelers, which is crucial for riders who need to plan their journeys and ensure that they can reach their destination without running out of charge. The proposed solution in this paper concentrates on using machine learning techniques to predict the range. Additionally, an application has been developed for this research purpose that collects data from users and uses it to train the machine learning model. Multiple experiments have been conducted using an e-scooter for this research. We place a greater emphasis on the data that are obtained from our application using smartphone sensor or APIs and minimal engagement from the user. Therefore, the dataset we collected contains riders' behavior, road condition, weather, and battery information. Several machine learning techniques have been used for training and the results have been compared with an actual trip dataset. The method that was proposed demonstrates that it is possible to obtain an accurate range prediction even when one lacks access to specific and in-depth technical knowledge of the two-wheeler and the results are promising. The additive regression model combined with the support vector machine produced the best results of all the models. The study presented in this paper achieved a mean absolute error of 0.13 using the testing dataset, which is significantly lower than the errors reported in other studies. Moreover, the results of the predictions that were expected are consistent. Then, the selected ML model has been set on the cloud to make prediction and provide the result to our application.  
We addressed the fact that our solution is able to deliver information about the range of electric two-wheelers. Because we are aware that our findings are based on a single dataset, we believe that further research involving a variety of datasets is required in order to validate the outcomes of our analysis. This is because we are aware that our conclusions are derived from a single dataset, mostly due to the fact that there was only ever one electric scooter utilized to compile that dataset. As part of our ongoing research, we intend to carry out additional studies using a variety of two-wheelers now available on the market. In addition, there is the possibility of taking into account additional parameters that have an impact on the range. However, since our ML model is user-specific, it needs to be updated regularly to ensure accurate predictions. This is because the factors that affect the range of  
Figure 8: Comparison of number of parameters, such as technical and nontechnical parameters, [PERSON] et al. [14], [PERSON] et al. [15], [PERSON], et al. [35].  
electric two-wheelers can vary over time and the model needs to be trained on new data to capture these changes. To address this challenge, we plan to use reinforced learning.  
In conclusion, more machine learning methods are something that can be researched for this strategy. Since the fear of running out of battery power is one of the primary barriers preventing widespread use of electric vehicles, it is essential that efforts be maintained to enhance range prediction in order to make energy usage more sustainable.  
Conceptualization, A.A.; Methodology, A.A. and M.S.A.; Software, A.A. and M.S.A.; Validation, M.S.A.; Investigation, M.S.A.; Resources, A.A.; Data curation, M.S.A. and A.A.; Writing--original draft, A.A.; Writing--review & editing, M.S.A. and C.C.; Supervision, C.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.