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

Abstract

This article proposes a method based on the Kalman filter to improve the accuracy of the CO2 measurement in driving cycles such as worldwide harmonized light vehicles test cycles or real driving cycles which are 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 electronic control unit 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 allows to compute the CO2 emissions from the fuel consumption estimation of the electronic control unit 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 Kalman filter. 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 worldwide harmonized light vehicles test cycles. The technique thus has further potential application to replicating real driving cycles on the chassis dynamometer for real driving emission testing.

Adaptive control concepts using radial basis functions and a Kalman filter for embedded control applications

“…The KF-based approaches have shown to provide a more systematic way for updating the look-up tables parameters. This has been further been demonstrated in Guardiola et al 16 to improve the accuracy of CO 2 measurement in real driving cycles, which are subject to loss in accuracy due to dynamic limitation of the analyzers. However, one of the limitations of adaptive look-up table approaches is that they are usually limited to two parameters.…”

“…The Kalman filter based approaches have already been proven to be computationally efficient and suitable for engine control applications in Guardiola, Ho¨ckerdal and Guardiola et al 8,14,16 Therefore, Kalman filter was considered here for further investigations in combination with RBFs. As mentioned in the objective of this work, the learned value should not be susceptible to noise and should be accurate.…”