GHG Emission Reduction Potential of LDV Technologies in the EU, 2020-2025

Published: 2012.11.05

Ricardo, Inc.

The ICCT contracted with Ricardo, Inc., to assess the effectiveness of future light-duty vehicle technologies on vehicle performance and greenhouse gas (GHG) emissions. The scope of the project was to develop and execute an objective, independent analytical study of technologies likely to be available in the 2020–2025 timeframe.

The study covers several LDV classes typical for the European market, including the B Class, C Class, D Class, Small Crossover Utility Vehicle (CUV), Small N1 light commercial, and Large N1 light commercial vehicle segments. For each vehicle class, several combinations of powertrain architectures, engines, and transmissions were defined and studied. The powertrain architectures included conventional (with stop-start functionality), P2 Hybrid, and Powersplit Hybrid. The engines studied include several advanced concepts, including spark-ignited engines with direct injection and turbocharging; advanced European diesels; and Atkinson cycle engines for hybrids. The transmissions studied include advanced automatic transmissions and dual-clutch transmissions, plus the types used with the two hybrid architectures. GHG emissions were evaluated over the New European Driving Cycle (NEDC), the Japanese JC08 cycle, and the US FTP cycle.

The final report of the project can be found here.

To permit a more effective assessment of the simulation results, they were fit to response surface models (RSM) that define functions that relate results to values of the discrete and continuous input factors. These RSM were incorporated into a Data Visualization Tool to allow users to evaluate the effectiveness of technology packages in their potential to reduce GHG emissions. Because the Data Visualization Tool has a simpler representation of vehicle performance than hundreds of thousands of simulations results, it allows a user to assess quickly and efficiently the interactions between technologies in ways that would not be easily identified in individual simulations. The Data Visualization Tool can be downloaded here. An accompanying User Guide is here.

Contact: Peter Mock