In order to improve the fuel economy of extended range electric motorcycles and address the issue of poor adaptability to working conditions in the traditional equivalent consumption minimization strategy, an adaptive equivalent consumption minimization strategy (A-ECMS) was proposed, and a method involving introducing a proportional-integral controller to dynamically and adaptively adjust the equivalent factor based on battery state of charge (SOC) feedback, while using a genetic algorithm to optimize controller parameters and comparing the control effects of the proposed strategy with other strategies under different standard working conditions was used to investigate the performance of the strategy. The results show that under three typical working conditions, the proposed energy management strategy has an average comprehensive fuel consumption of 1.42 L/(100 km), which is 26.04% lower than the commonly used rule-based strategy in engineering. The average final SOC deviation of A-ECMS is only 0.17%, with the maximum SOC fluctuation range controlled within 0.69%. Meanwhile, the average single-step calculation time of A-ECMS is 25 ms. It is concluded that the proposed A-ECMS can not only effectively improve fuel economy but also maintain the balance of battery SOC and has the potential for online application, providing a reference for the design and optimization of energy management strategies for extended range electric motorcycles.
XIA Tian-zhi, LI Ding-li, CUI Shi-ya, LIU Zhuo, LIU Qiang.
Energy Management Strategy for Extended Range Electric Motorcycle Based on Adaptive ECMS Algorithm[J].
Science Technology and Engineering, 2026, 26(21): 9272-9282 DOI:10.12404/j.issn.1671-1815.2505118
等效因子的取值能反映不同能量源的使用倾向,是ECMS中影响整车燃油经济性和电池荷电状态(state of charge, SOC)平衡的重要参数[5]。不少学者对等效因子的优化开展了研究。Xie等[6]提出一种数据驱动的ECMS,利用人工神经网络确定等效因子。邓涛等[7]提出基于改进型灰色预测的车速预测算法和基于卡尔曼滤波估计坡度的线性拟合函数的坡度预测算法,构建了基于道路工况预测的自适应ECMS。Lu等[8]提出基于长短期记忆网络多信号车速预测的模型预测控制与ECMS相结合的方法,利用滚动优化调整等效因子。
考虑参数优化结果对不同工况的适应性[16],选取在摩托车相关测试和标准中常用的世界摩托车测试循环(world motorcycle test cycle, WMTC)作为策略的仿真工况对PI参数进行优化。优化前的PI参数参考文献[17]的研究,设定Kp、Ki的初始值分别为0.5、0.03。上述初始参数可视为工程中常见的经验整定结果,经过遗传算法的优化后,收敛到一组稳定的最优参数,${K}_{p}^{\mathrm{*}}$为0.46,${K}_{i}^{\mathrm{*}}$为0.013。
为了验证所提出的策略在不同典型工况下燃油经济性、SOC稳定性和运行时效性的表现,选取3种具有代表性的标准工况进行仿真,分别为:WMTC、中国典型城市公交循环工况(Chinese typical city bus cycle, CCBC),以及城市道路循环工况(urban dynamometer driving schedule, UDDS)。CCBC工况中的高频启停和低速行驶的特性可用于评估摩托车在类似中国城市道路环境下的性能;UDDS工况包含连续变化的低速、启停和瞬时加减速等情况,贴近摩托车的驾驶风格,广泛应用于轻型车辆的续航和燃油经济性等测试中。
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