A heavy-duty vehicle carbon emission predictive model based on real-road data
Global warming is directly related to heavy-duty vehicle fuel consumption and green-house gas(carbon dioxide(CO2)mainly)emissions,which are certified on the vehicle chassis dynamom-eter in China.Currently,vast amounts of vehicle real-road data from the portable emission measurement system(PEMS)and remote monitoring system are being collected worldwide.A binning-reconstruction calculation model is proposed,to predict the chassis dynamometer fuel consumption and CO2 emissions with real-road data.The engine's real fuel consumption level can be reflected by binning and clustering the real-road fuel consumption data based on the engine's torque and speed.The model is validated through chassis dynamometer and real-road data(PEMS test and remote monitoring data).The results show that for accumulated fuel consumption based on the on-board diagnostic(OBD)data stream,a pre-dictive relative error less than 5%is expected for the present model.For bag sampling results,the pro-posed model's accuracy is expected to be within 10%.
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