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- 저자 : Yingze Yang (검색결과 2 건)
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Yingze Yang,Lu Xiong,Weirong Liu,Kai Gao,Zhiwu Huang 한국정밀공학회 2018 International Journal of Precision Engineering and Vol.19 No.10
The inconsistent braking force will result in collision compacts between adjacent freight cars of heavy haul train, which utilizes ECP as brake system. Moreover, the rigid connection mode aggravates the negative effect. In practical applications, the brake pipe (BP) pressure sensor is used as the feedback in the closed loop brake force control of the ECP brake, but there is no sensor in the brake cylinder (BC), whose pressure is the control target, and these factors will contribute the longitudinal impacts among the freight cars. Therefore, an accurate and fast braking force control is extremely important for ECP brake. Firstly, as for the highly nonlinear ECP system of heavy haul train, an energy-based globally stable pressure observer for ECP brake is proposed. And then, according to the pressure of BC estimated by the observer, an enhanced sliding-mode control algorithm is applied to realize the fast and precise braking force control. Finally, experimental results are presented to demonstrate and validate effectiveness of the proposed observer and control algorithm.
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Zhiyong Zheng,Jun Peng,Kunyuan Deng,Kai Gao,Heng Li,Bin Chen,Yingze Yang,Zhiwu Huang 전력전자학회 2019 ICPE(ISPE)논문집 Vol.2019 No.5
Lithium-ion battery remaining useful life (RUL) is a key parameter on battery management system. Many machine learning methods are applied to RUL predictions, but they generally suffer from two limitations: (i) the extracted features fail to reflect the information hidden in the historical degradation status, and (ii) the accuracy cannot be guaranteed in the evaluation of battery degradation due to the non-linearity. In this paper, a new prediction method is proposed combining the time window (TW) and Gradient Boosting Decision Trees (GBDT). First, the energy (VCE) and the fluctuation index (VFI) of voltage signal are verified and selected as features. Then, a TW based feature extraction method is designed to extract features from the historical discharge process. After that, GBDT is adopted to model the relation of features and RUL. The proposed method is implemented on a recognized battery degradation dataset, and the advantages in accuracy are proven.
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