用机器学习公平比较电动车与燃油车的真实排放
Credible CO2 Comparisons: A Machine Learning Approach to Vehicle Powertrain Assessment
- 构建双模型框架，固定驾驶条件对比动力系统排放
- 通过反事实场景计算，实现电动车在燃油车路径下的排放预测
- 适合政策制定者和车企做真实路况碳排放评估
道路运输脱碳需要一致且透明的车辆技术碳排放比较方法。本文提出一种基于机器学习的框架，可在相同真实驾驶条件下对内燃机汽车（ICEVs）和电动汽车（EVs）进行直接、可比的操作评估。该方法通过固定实测速度曲线和环境背景，隔离出技术特异性影响，实现动力系统性能的公正比较。采用循环神经网络分别训练两类车辆模型，学习从驾驶上下文变量（速度、加速度、温度）到实际驱动变量（扭矩、油门）及瞬时二氧化碳当量排放率的映射关系。该结构支持构建反事实情景，回答：若电动车遵循燃油车的行驶轨迹，其将产生多少排放？通过统一瞬时排放指标，该框架实现了可复现、可信的车辆动力系统碳绩效评估，为真实运行条件下数据驱动的车辆碳表现分析提供了可扩展基础。
原文摘要 · Abstract (English)
Decarbonizing road transport requires consistent and transparent methods for comparing CO2 emissions across vehicle technologies. This paper proposes a machine learning-based framework for like-for-like operational assessment of internal combustion engine vehicles (ICEVs) and electric vehicles (EVs) under identical, real-world driving conditions. The approach isolates technology-specific effects by holding the observed speed profile and environmental context fixed, enabling direct comparison of powertrain performance. Recurrent neural network models are trained independently for each domain to learn the mapping from contextual driving variables (speed, acceleration, temperature) to internal actuation variables (torque, throttle) and instantaneous CO2-equivalent emission rates. This structure allows the construction of counterfactual scenarios that answer: What emissions would an EV have generated if it had followed the same driving profile as an ICEV? By aligning both vehicle types on a unified instantaneous emissions metric, the framework enables fair and reproducible evaluation of powertrain technologies. It offers a scalable foundation for credible, data-driven assessments of vehicle carbon performance under real-world operating conditions.
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