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<p>To improve the fuel economy of range-extended electric tractors and promote the development of low-pollution, high-efficiency green agricultural machinery, this paper proposes a Predictive SOC-based adaptive equivalent fuel consumption minimization strategy (PSA-ECMS). First, the dynamic programming (DP) algorithm is employed to analyze the optimal power distribution sequence under actual tractor operating conditions, constructing a dataset that integrates operational characteristics and SOC variation patterns. Second, a BP neural network enhanced by the goat optimization algorithm (GOA-BP) establishes a power-SOC dynamic mapping model to predict future SOC reference trajectories. Finally, within the PSA-ECMS framework, adaptively adjusts the equivalent factor based on the predicted SOC sequence to optimize power distribution in the range-extended electric tractor. Simulation results demonstrate that the SOC variation trend under the proposed strategy aligns closely with DP-optimized results. Compared to the conventional ECMS and adaptive ECMS (A-ECMS), PSA-ECMS achieves fuel consumption reductions of 5.54 % and 3.42 %, respectively, demonstrating superior fuel economy.</p>

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Research on energy management strategy of range-extended electric tractor incorporating SOC reference trajectory prediction

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • Fan Zhang,
  • Guiyong Wang,
  • Hanbai Zhang,
  • Dongli Wang

摘要

To improve the fuel economy of range-extended electric tractors and promote the development of low-pollution, high-efficiency green agricultural machinery, this paper proposes a Predictive SOC-based adaptive equivalent fuel consumption minimization strategy (PSA-ECMS). First, the dynamic programming (DP) algorithm is employed to analyze the optimal power distribution sequence under actual tractor operating conditions, constructing a dataset that integrates operational characteristics and SOC variation patterns. Second, a BP neural network enhanced by the goat optimization algorithm (GOA-BP) establishes a power-SOC dynamic mapping model to predict future SOC reference trajectories. Finally, within the PSA-ECMS framework, adaptively adjusts the equivalent factor based on the predicted SOC sequence to optimize power distribution in the range-extended electric tractor. Simulation results demonstrate that the SOC variation trend under the proposed strategy aligns closely with DP-optimized results. Compared to the conventional ECMS and adaptive ECMS (A-ECMS), PSA-ECMS achieves fuel consumption reductions of 5.54 % and 3.42 %, respectively, demonstrating superior fuel economy.