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Dual-layer adaptive power management strategy for E-tractor incorporating operating information and deep reinforcement learning

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

Abstract

Electric tractors are crucial for sustainable agriculture, but optimizing their power management strategies (PMS) to achieve energy savings remains challenging. This paper proposes an adaptive power distribution method of hybrid energy storage system (HESS) for electric tractors to further reduce battery capacity degradation and power loss costs. Firstly, a dataset based on the tractor's actual plowing operating conditions was created. Then, an operation condition recognition (OCR) method was designed for electric tractors by capturing the characteristic parameters of plowing operation information. Subsequently, a dual-layer adaptive PMS, termed OCR-SAC PMS, is established by integrating OCR and deep reinforcement learning (DRL) within a comprehensive optimization framework focused on minimizing operating costs. The Soft Actor-Critic (SAC) algorithm is utilized to continuously optimize power allocation. Simulation and experimental results demonstrate that the OCR achieves a recognition accuracy of 98 %. Furthermore, the proposed OCR-SAC PMS reduces operating costs by over 13 %, effectively suppresses battery peak power transients by more than 11.61 %, and reduces peak current transients by over 17.14 % compared to conventional PMSs. Additionally, optimizing the PMS through OCR results in a 70 % reduction in SAC training time.

Suggested Citation

  • He, Xionglin & Yu, Qiang & Jiang, Zihong & Tan, Yilin & Chen, Yongji & Xie, Bin & Wen, Changkai, 2025. "Dual-layer adaptive power management strategy for E-tractor incorporating operating information and deep reinforcement learning," Energy, Elsevier, vol. 319(C).
  • Handle: RePEc:eee:energy:v:319:y:2025:i:c:s0360544225005845
    DOI: 10.1016/j.energy.2025.134942
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    1. Castaings, Ali & Lhomme, Walter & Trigui, Rochdi & Bouscayrol, Alain, 2016. "Comparison of energy management strategies of a battery/supercapacitors system for electric vehicle under real-time constraints," Applied Energy, Elsevier, vol. 163(C), pages 190-200.
    2. Louback, Eduardo & Biswas, Atriya & Machado, Fabricio & Emadi, Ali, 2024. "A review of the design process of energy management systems for dual-motor battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    3. Barelli, L. & Bidini, G. & Pelosi, D. & Ciupageanu, D.A. & Cardelli, E. & Castellini, S. & Lăzăroiu, G., 2020. "Comparative analysis of AC and DC bus configurations for flywheel-battery HESS integration in residential micro-grids," Energy, Elsevier, vol. 204(C).
    4. Gao, Sichen & Zong, Yuhua & Ju, Fei & Wang, Qun & Huo, Weiwei & Wang, Liangmo & Wang, Tao, 2024. "Scenario-oriented adaptive ECMS using speed prediction for fuel cell vehicles in real-world driving," Energy, Elsevier, vol. 304(C).
    5. Tang, Qingsong & Hu, Manjiang & Bian, Yougang & Wang, Yuke & Lei, Zhiyong & Peng, Xiaoyan & Li, Keqiang, 2024. "Optimal energy efficiency control framework for distributed drive mining truck power system with hybrid energy storage: A vehicle-cloud integration approach," Applied Energy, Elsevier, vol. 374(C).
    6. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2015. "Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach," Applied Energy, Elsevier, vol. 139(C), pages 151-162.
    7. Yang, Chao & Sun, Tonglin & Wang, Weida & Li, Ying & Zhang, Yuhang & Zha, Mingjun, 2024. "Regenerative braking system development and perspectives for electric vehicles: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).
    8. Wilberforce, Tabbi & Anser, Afaaq & Swamy, Jangam Aishwarya & Opoku, Richard, 2023. "An investigation into hybrid energy storage system control and power distribution for hybrid electric vehicles," Energy, Elsevier, vol. 279(C).
    9. Luo, Zhen-hao & Xie, Bin & Tong, Yi-kun & Zhao, Zi-hao & Zheng, Bo-wen & Chen, Zhou-yang & Wen, Chang-kai, 2024. "Energy-saving drive control strategy for electric tractors based on terrain parameter identification," Applied Energy, Elsevier, vol. 376(PA).
    10. Shi, Junzhe & Xu, Bin & Shen, Yimin & Wu, Jingbo, 2022. "Energy management strategy for battery/supercapacitor hybrid electric city bus based on driving pattern recognition," Energy, Elsevier, vol. 243(C).
    11. He, Hongwen & Meng, Xiangfei & Wang, Yong & Khajepour, Amir & An, Xiaowen & Wang, Renguang & Sun, Fengchun, 2024. "Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    12. Wei, Changyin & Chen, Yong & Li, Xiaoyu & Lin, Xiaozhe, 2022. "Integrating intelligent driving pattern recognition with adaptive energy management strategy for extender range electric logistics vehicle," Energy, Elsevier, vol. 247(C).
    13. Zhang, Dongfang & Sun, Wei & Zou, Yuan & Zhang, Xudong & Zhang, Yiwei, 2024. "An improved soft actor-critic-based energy management strategy of heavy-duty hybrid electric vehicles with dual-engine system," Energy, Elsevier, vol. 308(C).
    14. Le, Tay Son & Nguyen, Tuan Ngoc & Bui, Dac-Khuong & Ngo, Tuan Duc, 2023. "Optimal sizing of renewable energy storage: A techno-economic analysis of hydrogen, battery and hybrid systems considering degradation and seasonal storage," Applied Energy, Elsevier, vol. 336(C).
    15. Hu, Lin & Tian, Qingtao & Zou, Changfu & Huang, Jing & Ye, Yao & Wu, Xianhui, 2022. "A study on energy distribution strategy of electric vehicle hybrid energy storage system considering driving style based on real urban driving data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    16. Wang, Shuai & Wu, Xiuheng & Zhao, Xueyan & Wang, Shilong & Xie, Bin & Song, Zhenghe & Wang, Dongqing, 2023. "Co-optimization energy management strategy for a novel dual-motor drive system of electric tractor considering efficiency and stability," Energy, Elsevier, vol. 281(C).
    17. Chen, Bo-Chiuan & Wu, Yuh-Yih & Tsai, Hsien-Chi, 2014. "Design and analysis of power management strategy for range extended electric vehicle using dynamic programming," Applied Energy, Elsevier, vol. 113(C), pages 1764-1774.
    18. Herrera, Victor & Milo, Aitor & Gaztañaga, Haizea & Etxeberria-Otadui, Ion & Villarreal, Igor & Camblong, Haritza, 2016. "Adaptive energy management strategy and optimal sizing applied on a battery-supercapacitor based tramway," Applied Energy, Elsevier, vol. 169(C), pages 831-845.
    19. Yongliang Zheng & Feng He & Xinze Shen & Xuesheng Jiang, 2020. "Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine," Energies, MDPI, vol. 13(2), pages 1-18, January.
    20. Hu, Jianjun & Wang, Yangguang & Zou, Lingbo & Wang, Zhouxin, 2023. "Adaptive rule control strategy for composite energy storage fuel cell vehicle based on vehicle operating state recognition," Renewable Energy, Elsevier, vol. 204(C), pages 166-175.
    21. Zhang, Zhen & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Yang, Jian & Jia, Qingxiao, 2023. "Double deep Q-network guided energy management strategy of a novel electric-hydraulic hybrid electric vehicle," Energy, Elsevier, vol. 269(C).
    22. Tian, Xiang & Cai, Yingfeng & Sun, Xiaodong & Zhu, Zhen & Xu, Yiqiang, 2019. "An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses," Energy, Elsevier, vol. 189(C).
    23. Zhou, Yujie & Huang, Yin & Mao, Xuping & Kang, Zehao & Huang, Xuejin & Xuan, Dongji, 2024. "Research on energy management strategy of fuel cell hybrid power via an improved TD3 deep reinforcement learning," Energy, Elsevier, vol. 293(C).
    24. Chang, Chun & Xu, Xiaoyu & Guo, Xinxin & Yu, Rong & Rasakhodzhaev, Bakhramzhan & Bao, Daorina & Zhao, Mingzhi, 2024. "Experimental and numerical study during the solidification process of a vertical and horizontal coiled ice storage system," Energy, Elsevier, vol. 298(C).
    25. Zhou, Jianhao & Xue, Siwu & Xue, Yuan & Liao, Yuhui & Liu, Jun & Zhao, Wanzhong, 2021. "A novel energy management strategy of hybrid electric vehicle via an improved TD3 deep reinforcement learning," Energy, Elsevier, vol. 224(C).
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