Fault Diagnosis in Hybrid Electric Vehicle Regenerative Braking System

Author

Sankavaram, Chaitanya ; Pattipati, B. ; Pattipati, Krishna R. ; Yilu Zhang ; Howell, Michael

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Connecticut, Storrs, CT, USA

Volume

2

fYear

2014

fDate

2014

Firstpage

1225

Lastpage

1239

Abstract

Regenerative braking is one of the most promising and environmentally friendly technologies used in electric and hybrid electric vehicles to improve energy efficiency and vehicle stability. This paper presents a systematic data-driven process for detecting and diagnosing faults in the regenerative braking system of hybrid electric vehicles. The diagnostic process involves signal processing and statistical techniques for feature extraction, data reduction for implementation in memory-constrained electronic control units, and variety of fault classification methodologies to isolate faults in the regenerative braking system. The results demonstrate that highly accurate fault diagnosis is possible with the classification methodologies. The process can be employed for fault analysis in a wide variety of systems, ranging from automobiles to buildings to aerospace systems.

Keywords

energy conservation; fault diagnosis; feature extraction; hybrid electric vehicles; regenerative braking; signal processing; statistical analysis; aerospace systems; automobiles; buildings; data reduction; electric vehicles; energy efficiency; environmentally friendly technology; fault classification methodology; fault detection; fault diagnosis; fault isolation; feature extraction; hybrid electric vehicle regenerative braking system; memory-constrained electronic control units; signal processing; statistical techniques; systematic data-driven process; vehicle stability; Batteries; Brakes; Fault diagnosis; Hidden Markov models; Mechanical power transmission; System-on-chip; Torque control; Automotive Systems; Automotive systems; Distance Measure; Fault Classification; Inference; Multiple Fault Diagnosis; Regenerative Braking System; distance measure; fault classification; inference; multiple fault diagnosis; regenerative braking system;

fLanguage

English

Journal_Title

Access, IEEE

Publisher

ieee

ISSN

2169-3536

Type

jour

DOI

10.1109/ACCESS.2014.2362756

Filename

6920008