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[期刊论文]

Stochastic velocity-prediction conscious energy management strategy based self-learning Markov algorithm for a fuel cell hybrid electric vehicle

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author:

Lin, Xinyou (Lin, Xinyou.) [1] | Ren, Yukun (Ren, Yukun.) [2] | Xu, Xinhao (Xu, Xinhao.) [3]

Indexed by:

EI

Abstract:

The stochasticity of vehicle velocity poses a significant challenge to enhancing fuel cell energy management strategy (EMS). Under these circumstances, a self-learning Markov algorithm-based EMS with stochastic velocity prediction capability is proposed. First, building upon the traditional offline-trained Markov model, a real-time self-learning Markov predictor (SLMP) is proposed, which collects historical data during the vehicle's driving process and continuously updates the state transition matrix on a rolling basis. It provides excellent prediction performance under stochastic driving cycles. and the impact of different prediction time-steps is analyzed. Subsequently, by employing sequential quadratic programming for optimal power allocation, the Stochastic Velocity-Prediction Conscious EMS for fuel cell hybrid electrical vehicle based on SLMP is constructed. Finally, the predictors and EMSs based on back-propagation neural network and offline-trained Markov are selected for performance comparison. The validation results indicate that the performance of SLMP improves as driving mileage accumulates. Meanwhile, the proposed Stochastic Velocity-Prediction Conscious EMS significantly improves economic performance in different driving cycles. Hardware-in-the-Loop experiments further validate the superior fuel cell efficiency and robustness of the proposed EMS. The key contribution lies in the real-time adaptability of the SLMP, which ensures improved prediction accuracy and economic performance as driving mileage accumulates. © 2025 Elsevier Ltd

Keyword:

Hybrid power Hybrid vehicles Markov chains Quadratic programming Stochastic systems

Community:

  • [ 1 ] [Lin, Xinyou]College of Mechanical Engineering & Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Ren, Yukun]College of Mechanical Engineering & Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 3 ] [Xu, Xinhao]College of Mechanical Engineering & Automation, Fuzhou University, Fuzhou; 350108, China

Reprint 's Address:

  • [lin, xinyou]college of mechanical engineering & automation, fuzhou university, fuzhou; 350108, china;;

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Source :

Energy

ISSN: 0360-5442

Year: 2025

Volume: 320

9 . 0 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

30 Days PV: 1

Affiliated Colleges:

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管理员  2025-06-03 12:10:18  追加

管理员  2025-03-26 18:53:35  创建

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