Parametric analysis and prediction of energy consumption of electric vehicles using machine learning

Abstract

Emission regulations for all automobiles have been introduced to reduce global warming caused by vehicles. Hybrid electric vehicles (HEVs) are being developed to address consumer demand for environmentally friendly automobiles with more power and better fuel efficiency. HEVs are propelled by a combination of an internal combustion engine (ICE) and one or more electric motors that draw power from a secondary battery, which is commonly a lithium-based battery. The fuel economy of such a hybrid drivetrain system can be enhanced above that of traditional ICE automobiles. Although the global world is now focussing on electric vehicles (EVs) over HEVs due to environmental pollution. In this study, a 1-dimensional model was developed for an electric vehicle (EV), and a parametric analysis was made for the eight different cycles using GT-Suite software. The parameters included motor power, state of charge of the battery, vehicle speed, distance travelled, and energy consumption. In light of the parametric analysis obtained using GT-Suite software, this paper also predicts the energy consumption of EV batteries using a neural network-based machine learning (ML) method. After selecting input parameters through a correlation coefficient index (CI) process, the proposed neural network-based prediction model has achieved 89% accuracy in predicting battery energy consumption which will help EV drivers to plan. It will also help automobile engineers to design more efficient and scalable EVs.