Research on energy management strategy of fuel-cell vehicles based on nonlinear model predictive control

被引:20
作者
Song, Ke [1 ,2 ]
Huang, Xing [1 ,2 ]
Cai, Zhen [1 ,2 ]
Huang, Pengyu [1 ,2 ]
Li, Feiqiang [3 ]
机构
[1] Tongji Univ, Sch Automot Studies, Shanghai 201804, Peoples R China
[2] Tongji Univ, Natl Fuel Cell Vehicle & Powertrain Syst Engn Res, Shanghai 201804, Peoples R China
[3] Beijing SinoHytec Co Ltd, Dongsheng S&T Pk,66 Xixiaokou Rd, Beijing 100192, Peoples R China
基金
中国国家自然科学基金;
关键词
Fuel cell vehicle; Energy management strategy; Model predictive control; Markov Monte Carlo method;
D O I
10.1016/j.ijhydene.2023.07.304
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Fuel cell hybrid electric vehicles (FCHEV) are one of the most promising new energy vehicles. The cost and lifetime of its powertrain have limited its commercial development. This paper proposed an energy management strategy based on nonlinear model predictive control (NMPC) technology to solve the economy and durability problem of FCHEVs. Based on Markov Monte Carlo(MCMC) method, a prediction model of multi-scale operating conditions is established, and dynamic programming(DP) is used to realize the optimal control in the predicted time domain. The "constant speed prediction" is innovatively adopted in the transition stage to improve the prediction accuracy and enable the model to be realized online. The ways to reduce calculating amount of NMPC are also discussed in this paper. This simplification leads to suboptimal fuel economy and durability of control system but can have obvious reduction in calculating time. The simulation results show that, compared with the thermostat strategy and the power following strategy, the degradation cost decrease of 11.1% and 23.9% and the total operation cost of NMPC decrease of 11.0% and 23.5% respectively. The NMPC strategy has better economy and durability than the rule-based energy management strategy, is close to the global optimal result obtained by dynamic programming and can meet the requirements of real-time control. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:1604 / 1621
页数:18
相关论文
共 50 条
  • [21] Research on Energy Control Strategy Based on Hierarchical Model Predictive Control in Connected Environment
    Tang X.
    Li S.
    Wang H.
    Duan Z.
    Li Y.
    Zheng L.
    Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2020, 56 (14): : 119 - 128
  • [22] Model Predictive Control Based Energy Management Strategy of Series Hybrid Electric Vehicles Considering Driving Pattern Recognition
    Hao, Jinna
    Ruan, Shumin
    Wang, Wei
    ELECTRONICS, 2023, 12 (06)
  • [23] A Hierarchical Energy Management Strategy Based on Model Predictive Control for Plug-In Hybrid Electric Vehicles
    Zhang, Yuanjian
    Chu, Liang
    Ding, Yan
    Xu, Nan
    Guo, Chong
    Fu, Zicheng
    Xu, Lei
    Tang, Xin
    Liu, Yadan
    IEEE ACCESS, 2019, 7 : 81612 - 81629
  • [24] Real-vehicle experimental validation of a predictive energy management strategy for fuel cell vehicles
    Kofler, Sandro
    Rammer, Georg
    Schnabel, Alexander
    Weingrill, David
    Bardosch, Peter
    Jakubek, Stefan
    Hametner, Christoph
    JOURNAL OF POWER SOURCES, 2025, 629
  • [25] Real-time energy management strategy for fuel cell/battery vehicle based on speed prediction DP solver model predictive control
    Liu, Caixia
    Li, Xiaoyu
    Chen, Yong
    Wei, Changyin
    Liu, Xiaoang
    Li, Kuo
    JOURNAL OF ENERGY STORAGE, 2023, 73
  • [26] Online energy management strategy of fuel cell hybrid electric vehicles based on rule learning
    Liu, Yonggang
    Liu, Junjun
    Qin, Datong
    Li, Guang
    Chen, Zheng
    Zhang, Yi
    JOURNAL OF CLEANER PRODUCTION, 2020, 260
  • [27] SoC Planner for Predictive Energy Management of Fuel Cell Vehicles
    Min, Qingyun
    Wei, Xiaodong
    Sun, Chao
    Ren, Qiang
    Liang, Biao
    Liu, Bo
    2021 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC), 2021,
  • [28] Energy Management Strategy of Fuel Cell Commercial Vehicles Based on Adaptive Rules
    Tao, Shiyou
    Peng, Zhaohui
    Zheng, Weiguang
    SUSTAINABILITY, 2024, 16 (17)
  • [29] Improving fuel economy and performance of a fuel-cell hybrid electric vehicle (fuel-cell, battery, and ultra-capacitor) using optimized energy management strategy
    Ahmadi, Saman
    Bathaee, S. M. T.
    Hosseinpour, Amir H.
    ENERGY CONVERSION AND MANAGEMENT, 2018, 160 : 74 - 84
  • [30] Management and control strategy of a hybrid energy source fuel cell/supercapacitor in electric vehicles
    Rezzak, Daoud
    Boudjerda, Nasserdine
    INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2017, 27 (06):