Energy Consumption Estimation of Electric Vehicles using different Navigational Parameters

Abstract

The demand of Battery Electric Vehicles (BEVs) is increasing at a very fast rate and the auto- mobile industry is considering them as the future of transport. They have number of advantages such as pollution free environment, lower cost of ownership and comfortable ride etc. but even with all these advantages customers are hesitant to buy BEVs due to driver s range anxiety, scarcity of charging infrastructure and long charging time. The driver s range anxiety is emerg- ing as the major concern for customers. Hence, a system has been developed to accurately predict the energy consumption for a BEV based on the different influencing factors such as traffic, road elevation, auxiliary loads, environmental temperature and wind speed etc. The proposed system contains two main modules, one for energy consumption estimation and the other for traffic speed prediction. For estimating the energy consumption based on different parameters two solutions have been proposed namely, Basic Energy Estimation (BEE) model and Improved Energy Estimation (IEE) model. The BEE model uses a Convolutional Neural Network (CNN) to predict en- ergy consumption based on three parameters namely, vehicle speed, tractive effort and road elevation. Multiple experiments with different variations are performed to explore the impact of number of layers and input feature descriptors. The IEE model uses a hybrid approach by combining a multi-channel CNN with Bagged Decision Tree for energy estimation. The IEE model provides better performance as it considers a number of other factors (such as auxiliary loads, battery s initial state of charge, wind speed and environmental temperature etc.) also for prediction. Unlike existing techniques, the proposed approaches do not require internal vehi- cle parameters from manufacturer and can easily learn complex patterns even from noisy data. Comparison of results with existing techniques shows that the developed IEE model provides better estimates with least mean absolute energy deviation of 0.08 ± 0.069.