PROCESS MONITORING IN HYBRID ELECTRIC VEHICLES BASED ON DYNAMIC NONLINEAR METHOD DOI Open Access
Yonghui Wang, Deprizon Syamsunur,

Ang Chun Kit

et al.

Istrazivanja i projektovanja za privredu, Journal Year: 2024, Volume and Issue: 22(2), P. 492 - 505

Published: Jan. 1, 2024

Highway third-level faults can significantly deteriorate the reliability and performance of hybrid electric vehicle (HEV) powertrains. This study presents a novel process monitoring method aimed at addressing this issue. We propose multivariate statistical based on dynamic nonlinear improvement, namely neural component analysis (DNCA). does not require establishment precise analytical models; instead, it only necessitates acquiring data from HEV Through numerical simulation real experiments, we demonstrate effectiveness approach in highway faults. The testing outcomes that DNCA outperforms traditional methods like principal (DPCA), conventional such as kernel PCA (KPCA) NCA, well DKPCA.

Language: Английский

Macro- and micro-spacetime feature-preference gated recurrent unit for remaining useful life prediction of electric motor in multiple working conditions DOI

Jiechen Sun,

Funa Zhou, Xiong Hu

et al.

Signal Image and Video Processing, Journal Year: 2024, Volume and Issue: 18(11), P. 7953 - 7968

Published: Aug. 1, 2024

Language: Английский

Citations

1
PROCESS MONITORING IN HYBRID ELECTRIC VEHICLES BASED ON DYNAMIC NONLINEAR METHOD DOI Open Access
Yonghui Wang, Deprizon Syamsunur,

Ang Chun Kit

et al.

Istrazivanja i projektovanja za privredu, Journal Year: 2024, Volume and Issue: 22(2), P. 492 - 505

Published: Jan. 1, 2024

Highway third-level faults can significantly deteriorate the reliability and performance of hybrid electric vehicle (HEV) powertrains. This study presents a novel process monitoring method aimed at addressing this issue. We propose multivariate statistical based on dynamic nonlinear improvement, namely neural component analysis (DNCA). does not require establishment precise analytical models; instead, it only necessitates acquiring data from HEV Through numerical simulation real experiments, we demonstrate effectiveness approach in highway faults. The testing outcomes that DNCA outperforms traditional methods like principal (DPCA), conventional such as kernel PCA (KPCA) NCA, well DKPCA.

Language: Английский

Citations

0