IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i10p2288-d1391421.html
   My bibliography  Save this article

A Predictive Energy Management Strategy for Heavy Hybrid Electric Vehicles Based on Adaptive Network-Based Fuzzy Inference System-Optimized Time Horizon

Author

Listed:
  • Benxiang Lin

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
    National Key Laboratory of Special Vehicle Design and Manufacturing Integration Technology, Beijing 100081, China)

  • Chao Wei

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
    National Key Laboratory of Special Vehicle Design and Manufacturing Integration Technology, Beijing 100081, China)

  • Fuyong Feng

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
    National Key Laboratory of Special Vehicle Design and Manufacturing Integration Technology, Beijing 100081, China)

  • Tao Liu

    (Inner Mongolia First Machinery Group Co., Ltd., Baotou 014030, China)

Abstract

Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of charge (SOC) trajectory obtained offline, which are only suitable for buses with known future driving conditions, this paper proposed an energy management strategy based on an adaptive network-based fuzzy inference system (ANFIS) that optimizes the time horizon length and enhances adaptability to driving conditions by integrating historical vehicle velocity, accelerations, and battery SOC trajectory. First, the vehicle velocity prediction model based on the radial basis function (RBF) neural network is used to predict the future velocity sequence. After that, ANFIS was used to optimize and update the length of the forecast time horizon based on the historical vehicle velocity sequence. Finally, compared with the fixed time horizon energy management strategy, which is based on model predictive control (MPC), the average calculation time of the energy management strategy is reduced by about 23.5%, and the fuel consumption per 100 km is reduced by about 6.12%.

Suggested Citation

  • Benxiang Lin & Chao Wei & Fuyong Feng & Tao Liu, 2024. "A Predictive Energy Management Strategy for Heavy Hybrid Electric Vehicles Based on Adaptive Network-Based Fuzzy Inference System-Optimized Time Horizon," Energies, MDPI, vol. 17(10), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2288-:d:1391421
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/10/2288/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/10/2288/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Tianyu & Liu, Huiying & Wang, Hui & Yao, Yongming, 2020. "Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles," Energy, Elsevier, vol. 198(C).
    2. Wieczorek, Maciej & Lewandowski, Mirosław, 2017. "A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm," Applied Energy, Elsevier, vol. 192(C), pages 222-233.
    3. Jichao Liu & Yanyan Liang & Zheng Chen & Hai Yang, 2023. "An ECMS Based on Model Prediction Control for Series Hybrid Electric Mine Trucks," Energies, MDPI, vol. 16(9), pages 1-26, May.
    4. Enyong Xu & Mengcheng Ma & Weiguang Zheng & Qibai Huang, 2023. "An Energy Management Strategy for Fuel-Cell Hybrid Commercial Vehicles Based on Adaptive Model Prediction," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    2. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    3. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    4. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    5. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    6. Chen, Zheng & Hu, Hengjie & Wu, Yitao & Zhang, Yuanjian & Li, Guang & Liu, Yonggang, 2020. "Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning," Energy, Elsevier, vol. 211(C).
    7. Jiajun Liu & Tianxu Jin & Li Liu & Yajue Chen & Kun Yuan, 2017. "Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs," Sustainability, MDPI, vol. 9(10), pages 1-18, October.
    8. Zhang, Bo & Zhang, Jiangyan & Shen, Tielong, 2022. "Optimal control design for comfortable-driving of hybrid electric vehicles in acceleration mode," Applied Energy, Elsevier, vol. 305(C).
    9. Horn, Michael & MacLeod, Jennifer & Liu, Meinan & Webb, Jeremy & Motta, Nunzio, 2019. "Supercapacitors: A new source of power for electric cars?," Economic Analysis and Policy, Elsevier, vol. 61(C), pages 93-103.
    10. Zhiwen Zhang & Jie Tang & Jiyuan Zhang & Tianci Zhang, 2024. "Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle," Energies, MDPI, vol. 17(6), pages 1-22, March.
    11. Li, Jianwei & Xiong, Rui & Mu, Hao & Cornélusse, Bertrand & Vanderbemden, Philippe & Ernst, Damien & Yuan, Weijia, 2018. "Design and real-time test of a hybrid energy storage system in the microgrid with the benefit of improving the battery lifetime," Applied Energy, Elsevier, vol. 218(C), pages 470-478.
    12. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    13. Petru Valentin Radu & Miroslaw Lewandowski & Adam Szelag, 2020. "On-Board and Wayside Energy Storage Devices Applications in Urban Transport Systems—Case Study Analysis for Power Applications," Energies, MDPI, vol. 13(8), pages 1-29, April.
    14. Jichao Liu & Yanyan Liang & Zheng Chen & Wenpeng Chen, 2023. "Energy Management Strategies for Hybrid Loaders: Classification, Comparison and Prospect," Energies, MDPI, vol. 16(7), pages 1-23, March.
    15. Iqbal, Mehroze & Laurent, Julien & Benmouna, Amel & Becherif, Mohamed & Ramadan, Haitham S. & Claude, Frederic, 2022. "Ageing-aware load following control for composite-cost optimal energy management of fuel cell hybrid electric vehicle," Energy, Elsevier, vol. 254(PA).
    16. Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    17. Zhou, Hongxu & Yu, Zhongwei & Wu, Xiaohua & Fan, Zhanfeng & Yin, Xiaofeng & Zhou, Lingxue, 2023. "Dynamic programming improved online fuzzy power distribution in a demonstration fuel cell hybrid bus," Energy, Elsevier, vol. 284(C).
    18. Yang, Bo & Yu, Tao & Shu, Hongchun & Dong, Jun & Jiang, Lin, 2018. "Robust sliding-mode control of wind energy conversion systems for optimal power extraction via nonlinear perturbation observers," Applied Energy, Elsevier, vol. 210(C), pages 711-723.
    19. Shi, Man & He, Hongwen & Li, Jianwei & Han, Mo & Jia, Chunchun, 2021. "Multi-objective tradeoff optimization of predictive adaptive cruising control for autonomous electric buses: A cyber-physical-energy system approach," Applied Energy, Elsevier, vol. 300(C).
    20. Ikeda, Shintaro & Ooka, Ryozo, 2019. "Application of differential evolution-based constrained optimization methods to district energy optimization and comparison with dynamic programming," Applied Energy, Elsevier, vol. 254(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2288-:d:1391421. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.