1. From lab-to-road & vice-versa: Using a simulation-based approach for predicting real-world CO2 emissions.
- Author
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Tsiakmakis, Stefanos, Fontaras, Georgios, Dornoff, Jan, Valverde, Victor, Komnos, Dimitrios, Ciuffo, Biagio, Mock, Peter, and Samaras, Zissis
- Subjects
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AUTOMOBILE emissions , *CARBON dioxide mitigation , *COMPUTER simulation , *STANDARD deviations , *AUTOMOBILE drivers , *ENVIRONMENTAL impact analysis - Abstract
Abstract CO 2 emissions of light-duty vehicles are certified over standardised, laboratory-based conditions and reported to the consumers. Such tests reflect specific operating conditions that differ from what an individual driver experiences. Vehicle simulation can bridge the gap and help provide customised, vehicle and trip-specific values. This study investigates the potential of using a simulation-based approach for calculating CO 2 emissions over real-world operation, when limited information and test-data are available. The methodology introduced in the European vehicle certification regulation since 2017 is used as a basis. Seven vehicles were tested over multiple on-road trips and in some cases on a chassis dyno. First, the analysis focused on the accuracy of the simulations when only limited information for the vehicle and its components are used. Subsequently, the model was calibrated on test data. The first case presented an error between 1.0% and 4.4% depending on the test, while the standard deviation was 10.0%. When using WLTP for calibration, the average error dropped to −2.9% to −0.2%, and the standard deviation decreased to 2.0%. When calibrating over on-road tests, the average error was 0.7% for the on-road tests and 4.5% for the WLTP. Highlights • A simulation-based method for estimating lab and real world CO 2 of LDVs is investigated. • 3 different approaches were investigated based on generic, real world or lab input data. • The average error remained low (<5%) and was <2% when calibrating on tests-data. • Test-data based calibration significantly reduces the uncertainty from 10% to about 3%. • The approach could serve for extrapolating certification data to real world or vice versa. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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