The objective in present study is to develop a regression analysis model to estimate real-world CO₂ emissions of light-duty diesel vehicles considering domestic road conditions. For regression analysis variables, OBD data such as vehicle speed, acceleration, engine speed (rpm), and engine power were used. Regression analysis results were compared with CO₂ emissions measured using PEMS on the test routes of the real driving emissions-light duty vehicles (RDE-LDV). In results, the vehicle speed and air/fuel data from the OBD signals maintained a linear relationship with the GPS and exhaust gas flowmeter-based vehicle speed and exhaust flow data. All determination coefficients were ≥0.99, indicating that the OBD data provided by the test vehicle in this study exhibited strong reliability. To investigate the accuracy of the regression equation estimated using the trip variables of the OBD data, the driving variables were substituted into the equation to obtain CO₂ estimations and the real CO₂ emissions measured using PEMS were compared. A strong linear relationship was observed between the regression equation-based CO₂ estimations and real CO₂ measurements. The determination coefficient was approximately 0.93, supporting the reliability of the estimation results.

本研究的目的是开发一个回归分析模型，以估算考虑国内道路条件的轻型柴油车的实际 CO ₂排放量。对于回归分析变量，使用了 OBD 数据，例如车速、加速度、发动机转速 (rpm) 和发动机功率。回归分析结果与CO ₂对比使用 PEMS 在实际驾驶排放轻型车辆 (RDE-LDV) 的测试路线上测量的排放。结果，来自 OBD 信号的车速和空气/燃料数据与 GPS 和基于废气流量计的车速和废气流量数据保持线性关系。各项判定系数均≥0.99，说明本次研究中试车提供的OBD数据具有较强的可靠性。为了研究使用 OBD 数据的行程变量估计的回归方程的准确性，将驱动变量代入方程以获得 CO ₂估计值，并比较使用 PEMS 测量的实际 CO ₂排放量。观察到基于回归方程的 CO ₂估计值和实际 CO ₂测量值。决定系数约为 0.93，支持估计结果的可靠性。