Title: Severity faults classification in hybrid electric vehicles' permanent magnet machines for prognostic purposes
Authors: Riham Ginzarly; Ghaleb Hoblos; Nazih Moubayed
Addresses: IRSEEM, ESIGELEC, Rouen, France ' IRSEEM, ESIGELEC, Rouen, France ' Department of Electricity and Electronics, Faculty of Engineering 1, Lebanese University, Tripoli, Lebanon
Abstract: Nowadays, automobile transportation is in a crisis due to the high price of gasoline and, in the future, the crises will worsen unprecedentedly. Hence, researches are oriented toward hybrid electric vehicles being a good alternative of conventional vehicles. Thus, assuring the health and proper operation of HEVs is a mission. This paper focus on the importance of HEV's permanent magnet machine fault identification where a survey of the different types of fault that may arise is illustrated, relating it to the useful parameters that should be measured and checked to detect the presence of each type of fault. The faults are classified according to their level of severity; a case study is conducted where it is shown that demagnetisation of magnet is the most sever fault taking into consideration the relative cost of magnet and the occurrence percentage of demagnetisation. The aim of this classification is to identify the machine's element that is mostly worthy of prognostic approach.
Keywords: hybrid electric vehicle; HEV; permanent magnet machine; PMM; faults; prognosis; finite element method; crack; flux density.
International Journal of Computer Aided Engineering and Technology, 2017 Vol.9 No.4, pp.480 - 496
Received: 09 Jan 2016
Accepted: 28 Mar 2016
Published online: 18 Aug 2017 *