ADAS and Fuel Economy: Competing Objectives?

[vc_row][vc_column][vc_column_text]A growing trend in the automotive market is the addition of advanced driver assistance systems (ADAS) to new vehicles. These ADAS features could be technologies such as lane departure warnings, emergency braking, or adaptive cruise control, all aimed at making vehicles safer and more comfortable to drive.

In addition to making vehicles safer and more comfortable, vehicle manufacturers are striving to make their vehicles more fuel efficient. We have seen that these two trends are causing a shift in the typical design process, where powertrain and vehicle departments are working more closely together than ever.

At the system level, even forecasting fuel economy demands an integrated, collaborative approach between powertrain and vehicle teams. To explore how this approach might work, we teamed up with Mechanical Simulation Corporation, the developers of CarSim.

Through open-source co-simulation provided by the Functional Mockup Interface (FMI), we were able to implement a workflow to couple powertrain and vehicle models in GT-SUITE and CarSim, respectively.

This allows engineers to predict complex phenomena, such as how a calibration of adaptive cruise control or traffic conditions might affect fuel economy, in a repeatable fashion, all virtually.

[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_single_image image=”62551″ img_size=”full” add_caption=”yes” alignment=”center”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]To demonstrate this, we setup a co-simulation study between GT-SUITE and CarSim, where the powertrain model in GT-SUITE provided torque to the vehicle model’s wheels in CarSim, and CarSim fully represented the chassis dynamics and virtual sensors that monitored the traffic environment.

Two main vehicles were considered: the lead vehicle, with an imposed vehicle speed that followed a standard driving cycle, and the main following vehicle, which used adaptive cruise control to target the lead vehicle at different following distances.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_single_image image=”62552″ img_size=”full” add_caption=”yes” alignment=”center”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]At the shorter following distances, the following vehicle trailed closely in each acceleration and deceleration phase of the driving cycle. At a longer following distance, the following vehicle still followed the lead vehicle, but was not as aggressive, resulting in smoother pedal position inputs and therefore smoother throttle control. We were surprised to see a large 3% fuel economy difference between the different following distances over the driving cycle.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_single_image image=”62553″ img_size=”full” add_caption=”yes” alignment=”center”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]By using this new approach to vehicle simulation, the coupled vehicle and powertrain models were able to find fuel economy effects that traditionally would be neglected by the vehicle dynamics engineer and missed by the powertrain engineer.

Written By: Jon Zeman[/vc_column_text][/vc_column][/vc_row]