This document discusses using smartphones and neural networks to model the driving behavior of electric vehicles. It aims to accurately and efficiently measure remaining battery charge. A smartphone with sensors logs driving data like speed, acceleration and jerk. A neural network then estimates energy consumption based on this training data. The objective is a low-cost method to help drivers maximize electric vehicle efficiency.

1. Modelling the Driving
Behaviour of Electric Vehicles
Using Smartphones
and Neural Networks
Prepared by
Rajanivetha G

2. Contents
• Introduction
• Problem statement
• Objective
• Related work
• Data logging
• Data processing
• Consumption estimation
• Reading and discussion
• Conclusion
• References
13-09-2015
Modelling the Driving behaviour of electric
vehicles using smart phones and neural
networks
2

3. Introduction
• Internal combustion vehicles
• Eco driving
• Electric vehicles
• Instrumented vehicles
13-09-2015
Modelling the Driving behaviour of electric
vehicles using smart phones and neural
networks
3

4. Problem Statement
• Maximize efficiency in vehicles
• Driving equipment to assist the driver
• Depends in the driving behaviour
• Use of electric vehicles
• Battery consumption measurement
13-09-2015
Modelling the Driving behaviour of electric
vehicles using smart phones and neural
networks
4

5. Objective
• Measuring the remaining battery charge
• Use of smart phones and neural networks
• Low cost, accurate and efficient
13-09-2015
Modelling the Driving behaviour of electric
vehicles using smart phones and neural
networks
5

6. Related work
• Analysing driving efficiency using sensors
• Mobile devices for speed and distance detection
• Mobile devices for accelerometers and GPS units
• Smartphone sensors to detect driving behaviour flaws
• Compare and conclude
13-09-2015
Modelling the Driving behaviour of electric
vehicles using smart phones and neural
networks
6

7. Data logging
Smart phone: Apple iPhone 4 equipped with a GPS receiver,
a accelerometer, a gyro and a compass.
• 1 Hz using an ad-hoc app
• Test route
• Instrumented data logging
13-09-2015
Modelling the Driving behaviour of electric
vehicles using smart phones and neural
networks
7
V0 Timestamp
V1 Latitude
V2 Longitude
V3 Speed
V4 Total acceleration
v5 Jerk

8. Data Processing
• Extraction of the Relevant Features
– Speed, acceleration, jerk
• Data correlation
13-09-2015
Modelling the Driving behaviour of electric
vehicles using smart phones and neural
networks
8

9. Consumption estimation
• Energy level consumption by a neural network
• Training a Single Layer Perceptron
• Estimating Error via Bootstrapping
• Sensitivity analysis
13-09-2015
Modelling the Driving behaviour of electric
vehicles using smart phones and neural
networks
9

10. Reading and discussion
• Collect more user data
• Analyse from different users
• Relationship between features and consumption if linear
• Analyse different driving patterns
• Car assistant application
13-09-2015
Modelling the Driving behaviour of electric
vehicles using smart phones and neural
networks
10

11. Conclusion
• Evaluation of eco-effeciency driving style
• Cost efficient
• Accuracy as expected
• Convenience and flexibility through neural networks
13-09-2015
Modelling the Driving behaviour of electric
vehicles using smart phones and neural
networks
11

12. References
• [8] T. M. Cerbe, A. Kuhnert, and S. Strube, “Fuel saving potential of car
• navigation systems,” in Proc. 16th ITS World Congr., Stockholm, Sweden,
• 2009.
• [9] “Manual de Conducción Eficiente para Conductores de Vehículos Industriales,”
• Madrid: Inst. para la Diversificación y Ahorro de Energía,
• Inst. para la Diversificación y Ahorro de Energía (IDAE), 2005.
• [10] A. Giszczak, “Driving tests for just-in-time navigation in road transport,”
• in Proc. 13th ITS World Congr., London, 2006.
• [11] G. A. Rhys-Tyler and M. C. Bell, “Influencing driver behavior for environmental
• benefit: The role of its technologies,” in Proc. 16th ITS
• World Congr., Stockholm, Sweden, 2009.
• [12] L. Xiaopeng and K. Tennant, “Vehicle energy management optimization
• using look-ahead three-dimensional digital road geometry,” in
• Proc. 16th ITS World Congr., Stockholm, Sweden, 2009.
• [13] E. Hellström, M. Ivarsson, J. Åslund, and L. Nielsen, “Look-ahead
• control for heavy trucks to minimize trip time and fuel consumption,”
• Control Eng. Practice, vol. 17, no. 2, pp. 245–254, 2009.
• [14] P. Kock, H. J. Welfers, B. Passenberg, S. Gnatzig, O. Stursberg, and A.
• W. Ordys, “Saving energy through predictive control of longitudinal
• dynamics of heavy trucks,” MAN–Kingston Univ.—Technical Univ.
• Munich, 2009.
• [15] E. B. Baum and D. Haussler, “What size net gives valid generalization?”
• Neural comput., vol. 1, no. 1, pp. 151–160, 1989.
• [16] A. E. Af Wåhlberg, “Fuel efficient driving training–state of the
13-09-2015
Modelling the Driving behaviour of electric
vehicles using smart phones and neural
networks
12

13. Thank you
13-09-2015
Modelling the Driving behaviour of electric
vehicles using smart phones and neural
networks
13