Novel method of implementation of solar based smart cycle and solar power consumption prediction using machine learning

Abstract

There are so many vehicles that came to influence the existing world, and we all know that automobile vehicles consume fuel for running. And their control system is based on the usual fossil fuel system. Nowadays, the demand for fuel and the cost of fuel is increasing. At the same time, fossil fuel can exceed only for a certain period. After that, we have to go for alternate methods. Therefore, we need other energy resources for transportation, so we are going for different types of energy like electrical energy and solar energy, which will be very useful to the world's future needs. The motor and the other assorted parts include sprocket, chain assembly, cycle, and the other easily available materials to serve and fulfill the project's purpose. It may be noted that solar energy is easily available free of cost, and the battery used is a rechargeable battery. In this project, the battery and solar panel are used to get electric power. The energy management may be predicted by using Machine Learning algorithms. The training models of the system can predict the actual of energy to run the vehicle. So, finally, it can give the system about the power consumption to run the vehicle. The electrical power from the battery and solar panel is used to operate the brushless DC motor with the help of a controller. In this arrangement, the wheel rotation is obtained by the front side of the brushless DC motor. Therefore, operating the cycle is very easy, and no workforce is required to drive the cycle. The operating system of the normal cycle is not altered. This system is a two-in-one system. In case of the battery is low, we dopedal manually. To avoid this, we can predict the energy consumption by training models in Machine Learning Algorithms. The system can predict the km to travel by using the stored power in the battery by using machine learning training models.