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Multi-objective optimization for energy-efficient management of electric Tractors via hybrid energy storage systems and scenario recognition

Author

Listed:
  • Yu, Qiang
  • He, Xionglin
  • Chen, Yongji
  • Jiang, Zihong
  • Tan, Yilin
  • Liu, Longze
  • Xie, Bin
  • Wen, Changkai

Abstract

The promotion of electric tractors faces significant challenges, including adapting powertrain systems to diverse operational conditions and optimizing energy efficiency and battery lifespan. This paper presents a hybrid energy storage system (HESS) architecture for electric tractors. And a multi-objective energy-efficient management strategy (EMS) based on plowing operation scenario recognition is proposed. The strategy involves developing an electric tractor model and a plowing operating condition (POC) cycle using real-world plowing data. Offline classification is performed using K-means clustering and Principal Component Analysis (PCA), while a Multilayer Perceptron Neural Network (MLPNN) is employed for online real-time scenario recognition. Additionally, a Multi-Strategy Improved Black-winged Kite Algorithm (MSIBKA) is developed to efficiently derive adaptive power allocation trajectories. Simulation and Hardware-in-the-Loop (HIL) experiments demonstrate that the proposed strategy effectively extends the lifespan of the HESS, smooths battery output, and reduces operating costs. Specifically, the supercapacitor supplies over 65 % of the peak power demand, reducing the battery C-rate by more than 10 %. Furthermore, the proposed system increases the state of charge (SOC) of the battery by at least 5 %, while reducing both operational costs and battery degradation costs by over 33.3 %. These results indicate that the proposed system and strategy provide substantial benefits in extending battery lifespan and enhancing energy efficiency.

Suggested Citation

  • Yu, Qiang & He, Xionglin & Chen, Yongji & Jiang, Zihong & Tan, Yilin & Liu, Longze & Xie, Bin & Wen, Changkai, 2025. "Multi-objective optimization for energy-efficient management of electric Tractors via hybrid energy storage systems and scenario recognition," Applied Energy, Elsevier, vol. 391(C).
    • Handle: RePEc:eee:appene:v:391:y:2025:i:c:s0306261925006282
    DOI: 10.1016/j.apenergy.2025.125898
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