The application of the CO2MPAS model for vehicle CO2 emissions estimation over real traffic conditions. (August 2022)

• Record Type: Journal Article
• Authors: Mogno, Caterina; Fontaras, Georgios; Arcidiacono, Vincenzo; Komnos, Dimitrios; Pavlovic, Jelica; Ciuffo, Biagio; Makridis, Michail; Valverde, Victor
• Is Part Of: Transport policy. Volume 124(2022)
• Page Start: 152
• Page End: 159
• Publication Date: 2022-08
• Subjects: Light duty vehicles -- CO2 emissions -- CO2MPAS -- EMEP/EEA -- Real-world -- Fuel consumption gap; Transportation and state -- Periodicals; Transportation -- Rates -- Periodicals; 388
• DOI: 10.1016/j.tranpol.2020.01.005
• Languages: English
• ISSNs: 0967-070X

Abstract

Abstract: A vehicle simulation model, CO2 MPAS supports the introduction of the new WLTP-based certification system for CO2 emissions in Europe. This paper investigates the possibility to use the underlying simulation methodology to accurately calculate CO2 emissions over real-world trips, thus to extend the use of the methodology beyond vehicle certification. As a reference, the analysis used measurement data obtained from four vehicles over two different routes under real-world driving conditions. The CO2 emissions were measured using portable emissions measurement systems. The formal CO2 MPAS methodology and two modified versions of it that require a reduced number of input data were assessed about their capacity to predict the measured CO2 emissions. The analysis focused on the accuracy and uncertainty of the three different methodology configurations. As an additional benchmark, the analysis considered the CO2 emissions estimates obtained from the EMEP/EEA Guidebook methodology used for emissions inventorying in Europe. Results show that the basic CO2 MPAS configuration demonstrates good performance in predicting CO2 emissions over on-road tests, reaching a prediction accuracy over an entire test trip of −0, 3% and a standard deviation of 3, 1%. The modified versions showed slightly higher biases up to 3% and uncertainties (5–7%), but remaining within reasonable limits considering the reduced number of inputs used in each case. Given its ability to predict CO2 emissions accurately on a local base, CO2 MPAS could be used for the prediction of instantaneous CO2 emissions in traffic micro-simulation exercises.