Estimación de la autonomía de un  vehículo eléctrico según el estilo de conducción

Authors

Alberto Díaz-Álvarez UNIVERSIDAD POLITÉCNICA DE MADRID. Instituto Universitario de Investigación del Automóvil (INSIA). Campus Sur de la UPM. Carretera de Valencia, km. 7 – 28031 Madrid Author
Francisco Serradilla-García UNIVERSIDAD POLITÉCNICA DE MADRID. Instituto Universitario de Investigación del Automóvil (INSIA). Campus Sur de la UPM. Carretera de Valencia, km. 7 – 28031 Madrid Author
José Javier Anaya-Catalán UNIVERSIDAD POLITÉCNICA DE MADRID. Instituto Universitario de Investigación del Automóvil (INSIA). Campus Sur de la UPM. Carretera de Valencia, km. 7 – 28031 Madrid Author
Felipe Jiménez-Alonso UNIVERSIDAD POLITÉCNICA DE MADRID. Instituto Universitario de Investigación del Automóvil (INSIA). Campus Sur de la UPM. Carretera de Valencia, km. 7 – 28031 Madrid Author
José Eugenio Naranjo-Hernández UNIVERSIDAD POLITÉCNICA DE MADRID. Instituto Universitario de Investigación del Automóvil (INSIA). Campus Sur de la UPM. Carretera de Valencia, km. 7 – 28031 Madrid Author

Eco-driving, Smartphone, GPS, Neural Networks, Energy Efficiency, Driver Modeling

This paper describes a process for estimating the

autonomy of electric vehicles based on driver

behavior.

To do this, 11 drivers will be monitored while

driving across an urban route, collecting some

parameters smartphones and their GPS and

accelerometer devices and processing them for

the extraction of a set of indicators that will

determine the driver’s driving profile. These

indicators, along with the consumption of the

vehicle, will be used to train a neural network to

predict variables such as passenger comfort and

the expected consumption in real traffic

conditions.

Since there is a direct relationship between

energy efficiency and aggressive driving, this

model is also suitable for the scope of driving

safety.