Improving CO2 emission assessment of Diesel based powertrains in dynamic driving cycles by data fusion techniques

The present paper proposes a method based on the Kalman filter (KF) to improve the accuracy of the CO2 measurement in driving cycles such as WLTC or real driving cycles which is inherently subject to a loss in accuracy due to the dynamic limitations of the CO2 analysers. The information from the analyser is combined with the ECU estimation of the fuel injection. The characteristics of Diesel engines and, in particular, the high efficiency of the combustion process and the Diesel Oxidation Catalyst (DOC) allows to compute the CO2 emissions from the fuel consumption estimation of the ECU by applying the carbon balance method assuming negligible HC and CO emissions. Then, the assessment of the CO2 analyser response time and accuracy allows to pose an estimation problem that can be solved by a KF. The application of the method to different driving cycles shows that analyser dynamic limitations may lead to an overestimation of the CO2 figures that can reach 4% in highly dynamic tests such as the WLTC. The technique thus has further potential application to replicating real driving cycles on the chassis dynamometer for Real Driving Emission (RDE) testing.