Title : 
Decision on prognosis approaches of Hybrid Electric Vehicles´ electrical machines
         
        
            Author : 
Ginzarly, Riham ; Hoblos, Ghaleb ; Moubayed, Nazih
         
        
            Author_Institution : 
IRSEEM, ESIGELEC, Rouen, France
         
        
        
            fDate : 
April 29 2015-May 1 2015
         
        
        
        
            Abstract : 
Hybrid Electric Vehicles (HEV) are becoming widely spread due to the predicted lack of fuel in addition to the pollution caused by the conventional vehicles. To overcome pollution and since it is expected that the lack of fuel will more increase, it is assessed that the production and use of HEVs will increase in the coming years. The main concern of HEVs is their reliability and availability; hence, assuring the health and proper operation of HEVs is a mission. Due to the importance of electrical machines health state in HEVs, this paper will present a survey on the available prognostic techniques that may be applied to assure an optimal and convenient operation of electrical machines in hybrid electric vehicle.
         
        
            Keywords : 
electric machines; hybrid electric vehicles; pollution; HEV; electrical machines; hybrid electric vehicles; prognosis approaches; prognostic techniques; Adaptation models; Data models; Hidden Markov models; Hybrid electric vehicles; Mathematical model; Neural networks; Prognostics and health management; Prognosis; electrical machine; hybrid electric vehicle;
         
        
        
        
            Conference_Titel : 
Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2015 Third International Conference on
         
        
            Conference_Location : 
Beirut
         
        
            Print_ISBN : 
978-1-4799-5679-1
         
        
        
            DOI : 
10.1109/TAEECE.2015.7113622