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author:

Quan, Shengwei (Quan, Shengwei.) [1] | Wang, Ya-Xiong (Wang, Ya-Xiong.) [2] | Xiao, Xuelian (Xiao, Xuelian.) [3] | He, Hongwen (He, Hongwen.) [4] | Sun, Fengchun (Sun, Fengchun.) [5]

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EI

Abstract:

Hydrogen supply and circulating in vehicular fuel cells is crucial for their output capability and lifetime. In this article, an enhanced multiple-input multiple-output (MIMO) model predictive control (MPC) scheme is proposed for hydrogen regulation based on vehicle speed-induced fuel cell current disturbance stochastic prediction. The Markov exponential smoothing law is first developed for the vehicle speed prediction. The forecasted fuel cell power demand is obtained through vehicle dynamics model and rule-based energy management to release the predictive stack current regarding as the disturbance of hydrogen control system. The discrete predicted current sequence is with stochastic features and typed into the predictive model of MPC which is on longer the length of control horizon. Two case studies are presented to discuss the influence of different speed sampling times on the hydrogen regulation result under the proposed enhanced MPC. The enhanced MPC has a better performance than the traditional MPC, and the control RMSE of which can be reduced by 44.09% in case 1 and 69.78% in case 2 during automotive driving cycles. A dSPACE MicroAutoBox hardware in loop (HIL) experiment was conducted and the results well matched with the simulation which has verified the real-time performance of the enhanced MPC scheme. © 2021 Elsevier Ltd

Keyword:

Feedback control Forecasting Fuel cells MIMO systems Model predictive control Predictive control systems Stochastic control systems Stochastic models Stochastic systems Vehicles

Community:

  • [ 1 ] [Quan, Shengwei]National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing; 100081, China
  • [ 2 ] [Wang, Ya-Xiong]National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing; 100081, China
  • [ 3 ] [Wang, Ya-Xiong]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 4 ] [Xiao, Xuelian]China North Vehicle Research Institute, Beijing; 100072, China
  • [ 5 ] [He, Hongwen]National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing; 100081, China
  • [ 6 ] [Sun, Fengchun]National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing; 100081, China

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Source :

Energy Conversion and Management

ISSN: 0196-8904

Year: 2021

Volume: 238

1 1 . 5 3 3

JCR@2021

9 . 9 0 0

JCR@2023

ESI HC Threshold:105

JCR Journal Grade:1

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

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Chinese Cited Count:

30 Days PV: 0

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