一项关于能源消耗分析和预测电动巴士在十字路口的研究,考虑驾驶行为
Aihong Lyu1, Huiming Zhang2, Yali Zhang3
1Vocational and Technical College, Xianyang Normal University, Xianyang, 712099, China.
Scientific reports
|December 29, 2025
概括
了解电动巴士 (E-Bus) 在十字路口的驾驶行为是优化能源消耗的关键. 这项研究表明,区分停车和不停车交叉路口可以提高电动公交车的能源预测准确度.
科学领域:
- 运输工程 运输工程
- 可持续的移动性 可持续的移动性
- 电动汽车技术 电动汽车技术
背景情况:
- 纯电动巴士 (E-Bus) 在十字路口的能源消耗模式尚不清楚.
- 驾驶行为显著影响能源效率,特别是在复杂的城市环境中,如公共汽车快速运输 (BRT) 路线.
- 准确预测电动巴士的能源消耗需要对十字路口相互作用进行详细分析.
研究的目的:
- 量化和分析进入十字路口的电动巴士驾驶行为和能源消耗之间的关系.
- 通过区分停车和不停车交叉路口来开发更准确的能源消耗预测模型.
- 研究机器学习模型在分类交叉路口类型和预测能源消耗方面的有效性.
主要方法:
- 从两条BRT路线收集了自然驾驶数据.
- 采用统计分析来比较停车和不停车交叉路口的能源消耗.
- 利用相关性分析,线性回归和机器学习 (XGBoost-KNN,KNN,XGBoost,GBDT,SVM) 来进行分类和预测建模.
主要成果:
- 该XGBoost-KNN模型在预测十字路口是否停车或不停车时达到84.4%的准确性.
- 区分十字路口类型显著提高了像XGBoost和SVM这样的能源消耗预测模型的准确性.
- 支持矢量机 (SVM) 在计算停止行为后,在能源消耗预测指标 (R2,MAE,RMSE) 中表现优于XGBoost.
结论:
- 在十字路口的驾驶行为,特别是停车模式,是E-Bus能源消耗的关键因素.
- 一种细分预测方法,区分停车和非停车交叉路口,提高了能源消耗预测的准确性.
- 这些发现为优化电动巴士运营提供了宝贵的见解,并为研究更复杂的交通条件铺平了道路.
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