Special Issue on Frontiers in Hybrid Vehicles Powertrain

Cammalleri, Marco; Dio, V. Di; Castellano, Antonella

doi:10.3390/app13116367

Cited by 1 publication

(1 citation statement)

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“…However, the fault detection and diagnosis (FDD) of HEV powertrain systems have not received adequate attention. Actually, a set of many powertrain devices may be extremely easy to lead to fault occurrence of HEV powertrain system [3]. The high incidence of faults in HEV powertrain systems aggravates not only energy consumption but also air pollution and global warming concerns [4].…”

Section: Introductionmentioning

confidence: 99%

Fault detection and separation of hybrid electric vehicles based on kernel orthogonal subspace analysis

Wang,

Deprizon,

Peng

et al. 2023

J Appl Eng Science

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Driving quality and vehicles safety of hybrid electric vehicles (HEVs) are two hot-topic issues in automobile technology. Nowadays, research focuses to more intelligent and convenient HEVs fault detection methods. This paper will focus on the fault detection of HEV powertrain system with a data-driven algorithm. Orthonormal subspace analysis (OSA) is a newly proposed data-driven method which adds the ability of fault separation. Nonetheless, the linear OSA algorithm cannot effectively detect powertrain system faults, since these faults present complex nonlinear characteristics. A new kernel OSA (KOSA) method is proposed to transform the nonlinear problem into a linear problem through the mapping of kernel function and the dimensionality reduction technique of OSA. Testing results on a nonlinear model and real samples of XMQ6127AGCHEVN61 HEV show that KOSA address the nonlinear problems and it performs better than OSA and kernel principal component analysis (KPCA)

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Section: Introductionmentioning

confidence: 99%