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author:

Ye, Wangcheng (Ye, Wangcheng.) [1] | Zhong, Shunbin (Zhong, Shunbin.) [2] | Shen, Ying (Shen, Ying.) [3] | Zhang, Xuezhi (Zhang, Xuezhi.) [4] | Wang, Ya-Xiong (Wang, Ya-Xiong.) [5]

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EI

Abstract:

The efficiency and dynamic response of air compressors are crucial for stability and lifespan of hydrogen fuel cells. A predictive control scheme with surge- and choke-constrained awareness is proposed to ensure safe and efficient operation of air compressors in this study. The proposed scheme consists of an efficiency enhancement model predictive control (EE-MPC), and an improved active disturbance rejection control (IADRC). Surge- and choke-constrained awareness is achieved by comparing predicted air flow with surge and choke limitations. Simultaneously, the EE-MPC is constrained with oxygen excess ratio (OER) and obtains optimal solution by searching active set. The reference flow and supply manifold pressure trajectories for IADRC are generated by EE-MPC. A designed piecewise differentiable nonlinear smoothing function is embedded in IADRC. The disturbances are estimated for coordinating flow and pressure control. Under China heavy-duty commercial vehicle test cycle for bus conditions, root-mean-squared errors (RMSEs) of flow and pressure are 3.27 g s−1 and 1.88 × 103 Pa, respectively, and the mean efficiency can be enhanced by 13.4% compared to the MPC with fixed OER. Finally, a controller hardware-in-the-loop test is conducted, with flow and pressure RMSEs of 2.48 g s−1 and 4.28 × 103 Pa between the test and simulation, respectively. © 2024 Wiley-VCH GmbH.

Keyword:

Commercial vehicles Compressors Disturbance rejection Efficiency Fuel cells Mean square error Model predictive control Predictive control systems

Community:

  • [ 1 ] [Ye, Wangcheng]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Zhong, Shunbin]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 3 ] [Shen, Ying]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 4 ] [Zhang, Xuezhi]Hebei Kingston Technology Co., Ltd, Xinji; 052360, China
  • [ 5 ] [Wang, Ya-Xiong]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China

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Source :

Advanced Theory and Simulations

Year: 2024

Issue: 6

Volume: 7

2 . 9 0 0

JCR@2023

CAS Journal Grade:4

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ESI Highly Cited Papers on the List: 0 Unfold All

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Chinese Cited Count:

30 Days PV: 2

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