Prediction of Power and Current for Self Charging E-Bicycle Using Machine Learning Algorithms

The development of transportation-related technologies that can significantly reduce fossil fuel consumption is one of the world's most critical needs. The excessive use of fossil fuels, which greatly contributes to pollution and global warming, poses a severe threat to our planet. An electric bicycle represents an alternate solution to mitigate this issue. Due to its superior performance life-cycle and significant advancements in the transportation sector, lithium-ion battery technology is rapidly gaining recognition as a sustainable battery option for E-Bicycles. This Paper aims to extend battery life by recycling energy during operation using a dynamo. By applying supervised machine learning techniques, power and current can be forecast to enhance production and consumption, thereby increasing the bicycle's speed.