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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
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    References listed on IDEAS

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    3. 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.
    4. 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.
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