Real time power management strategy for an all-electric ship using a predictive control model

被引:7
|
作者
Wang, Bo [1 ]
Peng, Xiuyan [1 ]
Zhang, Lanyong [1 ]
Su, Peng [2 ]
机构
[1] Harbin Engn Univ, Coll Intelligent Syst Sci & Engn, Harbin, Peoples R China
[2] China Ship Dev & Design Ctr, Wuhan, Peoples R China
关键词
ENERGY-MANAGEMENT; SYSTEM; DESIGN; IMPLEMENTATION; TURBINE;
D O I
10.1049/gtd2.12419
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This study proposes a power management strategy (PMS) that can efficiently and accurately address the nonlinear dynamics of ship power systems for all-electric ships (AESs). The design of a PMS can be considered naturally in a model predictive control framework. However, the control error caused by a model mismatch is a challenge of the PMS for physical system implementation. The proposed PMS is based on a complex system-level nonlinear model that ensures a minimum mismatch. In addition, an efficient optimization algorithm is proposed to efficiently apply the PMS. The proposed method is verified using real-time simulations with physical systems in various scenarios. The experimental results demonstrate the superior efficiency of the proposed optimization algorithm and the accuracy of the developed PMS for physical system implementation.
引用
收藏
页码:1808 / 1821
页数:14
相关论文
共 50 条
  • [21] Full Simulation Modeling of All-Electric Ship with Medium Voltage DC Power System
    Ku, Hyun-Keun
    Park, Chang-Hwan
    Kim, Jang-Mok
    ENERGIES, 2022, 15 (12)
  • [22] Energy Management for an All-Electric Aircraft via Optimal Control
    Wang, Mengyuan
    Kolluri, Suryanarayana
    Shah, Krishna
    Subramanian, Venkat R.
    Mesbahi, Mehran
    IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2023, 59 (02) : 1084 - 1095
  • [23] MPC-informed ECMS based real-time power management strategy for hybrid electric ship
    Xie, Peilin
    Tan, Sen
    Guerrero, Josep M.
    Vasquez, Juan C.
    ENERGY REPORTS, 2021, 7 : 126 - 133
  • [24] Real-Time Energy Management of the Electric Turbocharger Based on Explicit Model Predictive Control
    Zhao, Dezong
    Stobart, Richard
    Mason, Byron
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2020, 67 (04) : 3126 - 3137
  • [25] Real-Time Model Predictive Control for Shipboard Power Management Using the IPA-SQP Approach
    Park, Hyeongjun
    Sun, Jing
    Pekarek, Steven
    Stone, Philip
    Opila, Daniel
    Meyer, Richard
    Kolmanovsky, Ilya
    DeCarlo, Raymond
    IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2015, 23 (06) : 2129 - 2143
  • [26] All-Electric Ship Energy System Design Using Classifier-Guided Sampling
    Backlund, Peter B.
    Seepersad, Carolyn Conner
    Kiehne, Thomas M.
    IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, 2015, 1 (01): : 77 - 85
  • [27] Energy management and control strategy of ship diesel-electric hybrid power system
    Xiao N.
    Xu X.
    Zhou R.
    Xiao, Nengqi (xiaonengqi@126.com), 1600, Editorial Board of Journal of Harbin Engineering (41): : 153 - 160
  • [28] Model Predictive Control for Nonlinear Energy Management of a Power Split hybrid Electric Vehicle
    Shi, Dehua
    Wang, Shaohua
    Cai, Yingfeng
    Chen, Long
    Yuan, ChaoChun
    Yin, ChunFang
    INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2020, 26 (01) : 27 - 39
  • [29] A modified model-free-adaptive-control-based real-time energy management strategy for plug-in hybrid electric vehicle
    Liu, Xiaodong
    Guo, Hongqiang
    Du, Juan
    Zhao, Xuan
    ENERGY SCIENCE & ENGINEERING, 2022, 10 (10) : 4007 - 4024
  • [30] A Fast Nonlinear Model Predictive Control Strategy for Real-time Motion Control of Mechanical Systems
    Chen, Yutao
    Cuccato, Davide
    Bruschetta, Mattia
    Beghi, Alessandro
    2017 IEEE INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM), 2017, : 1780 - 1785