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A metaheuristic algorithm for model predictive control of the oil-cooled motor in hybrid electric vehicles

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  • Liu, Jiangchuan
  • Ma, Qixin
  • Zhang, Quanchang

Abstract

The current energy management methods for hybrid vehicles are primarily focused on matching the power flow between the engine and the motor. In order to further reduce overall energy consumption and extend the vehicle's lifespan, this paper utilizes model predictive control (MPC) in the energy management of hybrid vehicles to control the energy consumption and temperature of the oil-cooled motor, with a focus on studying the algorithm for solving the optimization problem in MPC. The main work includes: Establishing a simplified model for the oil-cooled motor and training the working condition prediction model based on the worldwide harmonized light vehicles test cycle (WLTC) conditions utilizing long short-term memory (LSTM) recurrent neural network technology. Furthermore, proposing a novel metaheuristic algorithm based on the temporal nature of the problem, multiple hierarchical clustering algorithm (MHCA). In comparison with typical metaheuristic algorithms, the MPC optimized using MHCA exhibits significantly reduced computation time, with an average time of 0.1967s, indicating that using MHCA can theoretically achieve real-time control and eliminate lag in actual control processes. Furthermore, compared to other optimization algorithms, the MHCA algorithm demonstrates the lowest correlation between computation time and operational conditions, highlighting its high robustness in ensuring the safety of practical control.

Suggested Citation

  • Liu, Jiangchuan & Ma, Qixin & Zhang, Quanchang, 2024. "A metaheuristic algorithm for model predictive control of the oil-cooled motor in hybrid electric vehicles," Energy, Elsevier, vol. 295(C).
    • Handle: RePEc:eee:energy:v:295:y:2024:i:c:s0360544224007965
    DOI: 10.1016/j.energy.2024.131024
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    References listed on IDEAS

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    1. Arora, Parul & Kumar, Himanshu & Panigrahi, Bijaya Ketan, 2020. "Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Zhang, Zhiqing & Wang, Su & Pan, Mingzhang & Lv, Junshuai & Lu, Kai & Ye, Yanshuai & Tan, Dongli, 2024. "Utilization of hydrogen-diesel blends for the improvements of a dual-fuel engine based on the improved Taguchi methodology," Energy, Elsevier, vol. 292(C).
    3. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    4. Haifeng Liu & Junsheng Ma & Laihui Tong & Guixiang Ma & Zunqing Zheng & Mingfa Yao, 2018. "Investigation on the Potential of High Efficiency for Internal Combustion Engines," Energies, MDPI, vol. 11(3), pages 1-20, February.
    5. Fan, Dongyan & Sun, Hai & Yao, Jun & Zhang, Kai & Yan, Xia & Sun, Zhixue, 2021. "Well production forecasting based on ARIMA-LSTM model considering manual operations," Energy, Elsevier, vol. 220(C).
    6. Zhang, Yuanjian & Chu, Liang & Fu, Zicheng & Xu, Nan & Guo, Chong & Zhao, Di & Ou, Yang & Xu, Lei, 2020. "Energy management strategy for plug-in hybrid electric vehicle integrated with vehicle-environment cooperation control," Energy, Elsevier, vol. 197(C).
    7. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    Full references (including those not matched with items on IDEAS)

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