Open AccessIntelligent driving range predictor for green transport
Author(s) -
Sravan Pai,
M R Sindhu
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/561/1/012110
Subject(s) - software deployment , battery (electricity) , driving range , incentive , range (aeronautics) , subsidy , state of charge , computer science , automotive engineering , grid , electric vehicle , government (linguistics) , power (physics) , simulation , environmental economics , engineering , operating system , market economy , linguistics , physics , geometry , mathematics , philosophy , quantum mechanics , economics , microeconomics , aerospace engineering
Deployment of large number of electric vehicles has been planned in the coming years for improving fuel economy and to meet emission standards. Government of India plans to introduce subsidy/tax rebate/incentive for EV users who utilizes electrical energy in an optimal manner. The major anxiety of people opting for EV is low driving range and the accessibility of connection to the utility grid. If a facility is available to monitor battery parameters to compute power requirements and to list out nearest charging stations, it will help EV users a lot. This paper proposes an intelligent monitoring and predicting scheme for battery state of charge, driving range and other useful information. The scheme is validated with MATLAB/Simulink simulation results.