机器学习框架用于预测混合动力和内燃车的燃料消耗和二氧化碳排放:比较分析和性能评估
Rania A Ibrahim1, Nahla E Zakzouk1
1Electrical and Control Engineering Department, College of Engineering and Technology, Arab Academy for Science and Technology, Alexandria, Egypt.
PloS one
|February 11, 2026
概括
本研究介绍了一种机器学习框架,用于准确预测轻型车辆的燃料消耗和排放. 它独特地结合了内燃机车辆和插电式混合动力车辆的数据,以提高概括性.
科学领域:
- 汽车工程 汽车工程
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 准确的车辆燃料消耗和排放数据对于性能基准测试,监管合规性和能源管理至关重要.
- 机器学习 (ML) 为复杂的非线性系统提供灵活,可扩展和自动化的解决方案,优于传统的基于模型的方法.
- 现有的研究往往仅限于内燃机车辆 (ICEV),限制了更广泛的适用性.
研究的目的:
- 开发一个预测机器学习 (ML) 框架,用于估计轻型车辆的燃油消耗和排放.
- 将ICEV和插电式混合动力电动汽车 (PHEV) 的数据集集集成到统一的建模工作流程中.
- 为了实现排放和燃料使用的双重预测,提供更全面的预测能力.
主要方法:
- 开发一个使用现实世界数据集的预测性ML框架.
- 在一个共同的建模工作流程中融合和整合ICEV和PHEV数据集.
- 在ML框架内实施整体方法,以捕捉复杂的数据关系.
主要成果:
- 拟议的ML框架准确估计了轻型车辆的燃料消耗和排放.
- 多种数据集 (ICEV和PHEV) 的整合提高了模型的概括性和可靠性.
- 双预测功能可以同时预测排放量和燃料消耗.
结论:
- 统一的ML框架为环境评估和可持续汽车开发提供了实用和可靠的工具.
- 该模型能够在不同车型 (ICEV,PHEV) 上进行概括,这使得它对政策决策非常有价值.
- 这种方法促进了对车辆排放和能源消耗的准确预测,支持了更绿色的交通举措.
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