基于BP神经网络的车辆碳排放测算研究

研究轻型车辆碳排放测算方法，分析车辆碳排放与运行工况关系。基于车辆实际行驶污染物排放(Real Drive Emission, RDE)车载测试数据，以CO2当量CO2e代表碳排放，分析得出碳排放速率随车速、比功率(Vehicle Specific Power, VSP)增大而上升；运用BP (Back Propagation)神经网络算法建立车辆碳排放与车速、加速度、比功率多参数间非线性关系测算模型，计算得出世界轻型车测试循环(World Light Vehicle Test Cycle,WLTC)、新欧洲行驶循环(New European Driving Cycle, NEDC)和中国轻型商用车行驶工况(China Light-duty Vehicle Test Cycle-commercial Car,CLTC-C)3种台架测试循环工况下的碳排放因子。比较发现3种台架测试循环工况下的碳排放因子均高于实际道路行驶碳排放因子，其中WLTC下碳排放因子最高，其次是NEDC，再是CLTC-C，原因是加速度越大、车速越高的测试工况导致碳排放增加。

A Research on Vehicle Carbon Emission Calculating Method Based on BP Neural Network

The carbon emission calculating method of light vehicle is studied, and the relationship between vehicle carbon emission and operating conditions analyzed. Based on the on-board test data of real drive emission (RDE) of vehicles, the carbon emission is represented by CO₂ equivalent CO_2e. It is indicated that the carbon emission rate increases with the increase of vehicle speed and specific power. The Back Propagation (BP) neural network algorithm is used to establish the nonlinear relationship between vehicle carbon emission and speed, acceleration and vehicle specific power (VSP), and calculate the carbon emission factors under three bench test cycle conditions of World Light Vehicle Test Cycle (WLTC), New European Driving Cycle (NEDC) and China Light-duty Vehicle Test Cycle-commercial Car (CLTC-C). It is found that the carbon emission factors under the three bench test cycle conditions are higher than the actual road driving carbon emission factors, among which the carbon emission factor under WLTC is the highest, followed by NEDC and CLTC-C. The reason is that the test conditions with higher acceleration and speed lead to increased carbon emissions.