# An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques
## 2 Related Works
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **E-Mobility Type** & **Approach** \\ \hline \([22]\) & E-bike & Mathematical approach based on battery consumption data to predict power consumption \\ \hline \([10]\) & E-bike & ANN approach based on vehicle, smartphone sensor, and geographic information to predict power consumption \\ \hline \([11]\) & Two-wheeeler & Simulink model approach based on vehicle and battery information to predict mileage \\ \hline \([8]\) & E-bike & Machine learning approach based on SOC, rider, and environment information to predict destination can be reached with current SOC or not \\ \hline \([12]\) & E-bike & Machine learning approach based on rider, traffic, and battery information to predict battery consumption \\ \hline \([23]\) & Scooter & Simulink model approach based on rider, vehicle, and battery information to predict mileage \\ \hline \([24]\) & Scooter & Simulink model approach based on rider, vehicle, and battery information to predict power and energy \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work summary. based on the kinematics of the vehicle with the velocity profile over time and, finally, in the global model, authors connected the battery model and dynamic model together. It seems that the global model carried out better results than other models. However, these experiments needed lots of input parameters, such as voltage, capacity, discharge current, battery weight, and many more, where riders may not know all the electronics data. In [23], the authors attempted a comparable model, resulting in an error range of approximately 0.36% to 1.1% in predicting range. Another similar model was also developed by authors in [24], where an electric scooter was studied to predict energy consumption and range through dynamometer test and simulation model. The simulation model shows that the electric scooter can cover a distance of up to 63,470 m. During the dynamometer tests, the scooter achieved an average range of 60,460 m, while, on normal on-road testing, it covered 62,830 m. The errors in comparison are 4.74% and 1.01% for the standard dynamometer and on-road tests, respectively. After reviewing simulation-based study, we shifted our focus to machine-learning-based study. We have discovered in [12] a machine learning model was used based on the battery information and sensor data from the e-bike. The authors from the University of Waterloo developed a project called WeBike, where an e-bike was prepared with 31 sensors and connected with an online map server through a GPS-equipped smartphone to collect data. They have organized several participants to collect data such as each participant's battery consumption rate, average speed, trip segments, traffic sign, average battery temperature, etc. Two prediction models such as mean prediction and linear regression are used for range prediction that took into account the behavior of the cyclist in addition to the route. Both model's root mean squared errors were around 5.3-9.7%. Moreover, two-wheelers do not come with multiple sensors unless someone upgrades by himself/herself like they prepared their own hardware set. We focused on data which can be easily accessible without any hassle from the rider and without any Bluetooth connection. The findings were good enough but there are other external factors as well, such as actual battery capacity, rider's speed, wind speed, and elevation, which also need to be studied, explained by the author. In another study [8], the authors worked with those external factors where they developed a system that can be installed in a smartphone. For the study purpose, a prototype e-bike was prepared where their motor control system, microcontroller, and battery were highlighted. The MCS app which they have developed can read battery information through Bluetooth as well as collect smartphone sensor data.