Condition monitoring of bidirectional DC-DC converter for hybrid electric vehicles

Al-Sheikh, Hiba; Bennouna, Ouadie; Hoblos, Ghaleb; Moubayed, Nazih

doi:10.1109/med.2014.6961354

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…The combination between those two systems may be series, parallel or mixed [1], [2]. The electric propulsion system contains the electric machine, the controller and the inverter [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Faults in hybrid electric vehicles' permanent magnet machines

Ginzarly¹,

Hoblos²,

Moubayed³

2015

2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)

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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 demagnetization of magnet is the most sever fault taking into consideration the relative cost of magnet and the occurrence percentage of demagnetization. Index Terms -Hybrid electric vehicle, permanent magnet machine, Faults.

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Section: Introductionmentioning

confidence: 99%

Faults in hybrid electric vehicles' permanent magnet machines

Ginzarly¹,

Hoblos²,

Moubayed³

2015

2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)

View full text Add to dashboard Cite

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Decision on prognosis approaches of Hybrid Electric Vehicles' electrical machines

Ginzarly¹,

Hoblos²,

Moubayed³

2015

2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)

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Condition Parameter Estimation for Photovoltaic Buck Converters Based on Adaptive Model Observers

Cen

Stewart

2017

IEEE Trans. Rel.

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DC-DC power converters such as Buck converters are susceptible to degradation and failure due to operating under conditions of electrical stress and variable power sources in power conversion applications such as electric vehicles and renewable energy. Some key components such as electrolytic capacitors degrade over time due to evaporation of the electrolyte. In this paper, a model-observer based scheme is proposed to monitor states of Buck converters and to estimate their component parameters such as capacitance and inductance. Firstly, a diagnosis observer is proposed, and the generated residual vectors are applied for fault detection and isolation. Secondly, component condition parameters such as capacitance and inductance are reconstructed using another novel observer with adaptive feedback law. Additionally, the observer structures and their theoretical availability are analyzed and proven. In contrast to existing reliability approaches applied in Buck converters, the proposed scheme perform online-estimation for key parameters. Finally, Buck converters in conventional DC-DC step-down and Photo-Voltaic applications are investigated to test and validate the effectiveness of the proposed scheme in both simulation and laboratory experiment. Results demonstrate the feasibility, performance and superiority of the proposed component parameters estimation scheme.

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Condition monitoring of bidirectional DC-DC converter for hybrid electric vehicles

Cited by 5 publications

References 16 publications

Faults in hybrid electric vehicles' permanent magnet machines

Faults in hybrid electric vehicles' permanent magnet machines

Decision on prognosis approaches of Hybrid Electric Vehicles' electrical machines

Condition Parameter Estimation for Photovoltaic Buck Converters Based on Adaptive Model Observers

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