Range extended for electric vehicle based on driver behaviour

Driver behaviour has been considered one of the main factors that contribute to increase fuel consumption, CO2 emissions, traffic accidents and causalities. Thus, the concept of detecting and classifying driver behaviour i s vital when tackling these challenges. Recognition of the driver behaviour is a difficult task as in the real-world, the driving behaviour is effected by many factors e.g. traffic, road conditions, duration of the journey etc. Many approaches have considered the use of Computational Intelligence techniques, to develop a driver behaviour detection system. In this paper we concentrate on the impact of driver behaviour on the energy consumption and thereby on the range of electric vehicles. A new architecture is proposed to show how computational intelligence techniques could interact with the contextual information collected from the vehicle, the driver and external environment. A neural network model is used to classify the driver behaviour, and then this classification is used in a fuzzy logic controller to make balanced managements to the range extender operation.

[1]  Mohd Zakwan Ramli Development of accident prediction model by using artificial neural network (ANN) , 2011 .

[2]  Hussein Zedan,et al.  A comprehensive survey on vehicular Ad Hoc network , 2014, J. Netw. Comput. Appl..

[3]  Michel André,et al.  The ARTEMIS European driving cycles for measuring car pollutant emissions. , 2004, The Science of the total environment.

[4]  Alicia L. Carriquiry,et al.  Driving behavior at a roundabout: A hierarchical Bayesian regression analysis , 2014 .

[5]  Ke Song,et al.  Modeling and Simulation of Power Train System for Extended-Range Electric Vehicle Using Bond Graphs , 2013 .

[6]  Chih-Sheng Hsu,et al.  Irregular Vehicle Behavior Warning Modules , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[7]  Yi Lu Murphey,et al.  Intelligent Vehicle Power Management: An Overview , 2008, Computational Intelligence in Automotive Applications.

[8]  Dirk Uwe Sauer,et al.  Operating Strategies for a Range Extender Used in Battery Electric Vehicles , 2013, 2013 IEEE Vehicle Power and Propulsion Conference (VPPC).

[9]  Zhaohui Wu,et al.  A Smart Car Control Model for Brake Comfort Based on Car Following , 2009, IEEE Transactions on Intelligent Transportation Systems.

[10]  Alex Pentland,et al.  Modeling and Prediction of Human Behavior , 1999, Neural Computation.

[11]  F. P. Brito,et al.  Analysis of four-stroke, Wankel, and microturbine based range extenders for electric vehicles , 2012 .

[12]  Hai Yu,et al.  Driving pattern identification for EV range estimation , 2012, 2012 IEEE International Electric Vehicle Conference.

[13]  Shijing Xu Investigation of EMS based on fuzzy logic controller for an ICE/battery/UC hybrid electric vehicle , 2011, 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC).

[14]  Britt A. Holmén,et al.  Characterizing the Effects of Driver Variability on Real-World Vehicle Emissions , 1998 .

[15]  Alex Pentland,et al.  Graphical models for driver behavior recognition in a SmartCar , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[16]  Alex Pentland,et al.  Driver behavior recognition and prediction in a SmartCar , 2000, Defense, Security, and Sensing.

[17]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[18]  Dejan Mitrovic,et al.  Reliable method for driving events recognition , 2005, IEEE Transactions on Intelligent Transportation Systems.

[19]  Li Jun,et al.  Design method and control optimization of an Extended Range Electric Vehicle , 2011, 2011 IEEE Vehicle Power and Propulsion Conference.

[20]  Rajesh Rajamani,et al.  Adaptive cruise control system design and its impact on highway traffic flow , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[21]  Tariq Muneer,et al.  The measurement of vehicular driving cycle within the city of Edinburgh. , 2001 .

[22]  Gurwinder Kaur,et al.  Neural Network Based Drowsiness Detection Using Electroencephalogram , 2013 .