Development of prediction methodology for CO2 emissions and fuel economy of light duty vehicle. (1st April 2022) - Song, Jingeun; Cha, Junepyo
Abstract: Fuel economy prediction models usually require vehicle specifications such as a fuel consumption map which are not publicly available. Therefore, the present study proposed a new data analyzing procedure to predict CO2 emissions and fuel economy using on-road driving data without confidential specifications. Vehicle specifications such as gear ratios and vehicle mass which are provided in a service manual and driving data such as vehicle speed and CO2 emission were used to develop the prediction model. Instead of the fuel consumption map, linear equations for each gear between wheel power and CO2 emissions were used to predict CO2 emissions for various driving modes. Since higher gears exhaust less CO2 than lower gears (the seventh gear exhausted 24.4% less CO2 than the first gear), the accuracy of fuel economy prediction was improved by applying the equations for each gear stage. The accuracy of the prediction was verified by comparing it with measurement data. The comparisons showed that the equations for each gear can predict the fuel economy more accurately than one equation representing the entire gear. In worldwide harmonized light vehicles test cycle (WLTC) mode, the former had a maximum error of 6.1%, but the latter showed an error of 17.9%. Highlights: A new data analyzing procedure to predict fuel economy was proposed. Linear equations for each gear between power and CO2 were used instead of fuel map. Accuracy of the prediction was verified by comparing it with measurement data.