An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques

Abstract

Electric two-wheelers are becoming increasingly popular across the world, particularly in cities where their small size and flexibility make them a viable option for navigating congested streets. One of the most challenging aspects of e-mobility on two-wheelers is precisely calculating their range. This might be an issue for riders who must go long distances or who have limited access to charging stations. Various factors can influence an electric two-wheeler range, making it challenging to predict how far it can travel on a single charge. To tackle this problem, most of the manufacturers offer range predictions based on both test data and real-world usage scenarios. However, these estimates are customized for specific vehicle models and testing parameters that may not apply in all circumstances. Additionally, it can be challenging to obtain comprehensive technical specifications for two-wheelers available in the market, as most manufacturers do not provide detailed technical information. Hence, it is crucial to address the challenge of range prediction for two-wheelers in general, which can be advantageous for riders. In this paper, we discuss the precise prediction of the remaining range of electric two-wheelers even without knowing detailed e-mobility technical information. An application is also developed only for this research purpose, which can provide navigation services. Our approach concentrates on user behavior, weather, road conditions, and the vehicle’s performance history, which is gathered through the application. The collected data are used to train the selected ML model on the cloud. We applied various machine learning algorithms before deploying in the cloud where the SVM algorithm demonstrated outstanding performance, with a mean absolute error of 150 m for an average distance of 7.46 km. Furthermore, the model’s performance was evaluated after deployment and tested having 130 m error on average.