根据每次行程的电力消耗来确定电动巴士的节能驾驶行为:一种机器学习方法
Sirui Nan1,2, Feixiong Liao2, Tiezhu Li3
1School of Transportation, Southeast University, Nanjing, 210096, China.
概括
这项研究使用机器学习来识别电动巴士 (EB) 的节能驾驶行为. 随机森林模型准确地确定了节能驾驶,提高了EB的可持续性.
科学领域:
- 可持续的运输可持续的运输
- 机器学习应用 机器学习应用
- 能源效率 能源效率 能源效率
背景情况:
- 电动公共汽车 (EB) 为柴油公共汽车 (DB) 提供了一个可持续的替代方案.
- 优化EB驾驶行为是减少能源消耗和扩展续航里程的关键.
- 了解驾驶模式对于制定节能策略至关重要.
研究的目的:
- 开发一种机器学习框架,用于识别EB的节能驾驶行为.
- 分析不同驾驶阶段 (路段,巴士站,十字路口) 的能源消耗.
- 建立一个可量化的指数来评估驾驶效率.
主要方法:
- 使用了EBs的历史驾驶数据.
- 开发了一个随机森林 (RF) 模型,以根据能源消耗对驾驶行为进行分类.
- 使用电能消耗因子 (ECF) 来对能源效率进行分类.
- 应用沙普利增量解释 (SHAP) 和部分依赖图 (PDPs) 进行模型解释.
主要成果:
- 射频模型在识别节能驾驶行为方面实现了超过90%的分类准确性.
- 确定了30-40公里/小时的速度范围是路段上EB车辆最节能的.
- 证明了该模型在将驾驶行为与能源消耗相关联方面的有效性.
结论:
- 拟议的机器学习框架准确地识别了EB的节能驾驶行为.
- 调查结果为优化驾驶策略提供了见解,以提高EB效率和可持续性.
- 该研究有助于推进环保交通系统的发展.
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