RDE in Simulation Saves Time and Money
Real-world testing of Real Driving Emissions (RDE) requires significant expenditure on staff, vehicles and test equipment, and takes time. The nature of real-world traffic and weather means that it is impossible to repeat test conditions reliably, so it becomes very expensive to achieve a statistically significant test result.
By using a simulator with rFpro and realistic swarm traffic, for example SUMO or PTV-Vissim, deterministic tests may be conducted in reproducible conditions including accurately modelled weather and traffic but, critically, with a human driver in control of the test vehicle.
Virtual RDE Testing
Virtual testing has become a central part of vehicle development. Used effectively, it enables critical decisions to be made early in an engineering program, prior to hardware, before committing to significant financial investment. Compared to testing an actual vehicle, it also offers the scope to evaluate the effects of a much wider range of possibilities under repeatable conditions, without weather or traffic variations. However, where human driver input is required, virtual testing is of little value unless that driver responds to the test scenario in exactly the same way as they would do in a ‘real world’ situation.
Human Drivers in Simulation
Creating a fully convincing virtual environment, in which a driver feels totally immersed, enables driver-in-the-loop (DIL) testing with a driving simulator to be used, for the first time, in areas where human reaction to a situation affects the vehicle’s behaviour. One such area, not previously associated with simulators, is emissions testing, because when RDE tests take the place of today’s artificial rolling road test cycles, variations between different drivers will significantly influence the results obtained.
The need to ensure, in advance, that a vehicle will achieve the target results during RDE testing threatens to add significant additional cost to the typical development program. Understanding driver-influenced variables, such as poor throttle modulation when cruising, or failure to anticipate traffic slowing down ahead, will be central to optimising the calibration of the vehicle. Quantifying the influence of driving habits on emissions is difficult and time consuming using on-road testing, but software developed by rFpro for DIL simulators creates a sufficiently high level of realism that drivers behave in a representative way. This gives manufacturers the necessary confidence that a car’s virtual emissions performance, in the hands of a human driver using the simulator, will be equivalent to its ‘real world’ results.
The introduction of RDE tests adds another level of complexity to vehicle testing, with emissions influenced by driving style and road conditions. The ability to evaluate the vehicle’s behaviour under repeatable laboratory conditions using drivers of different abilities and habits, in order to maximise confidence ahead of approval testing, can provide a massive saving in cost and time. AVL estimates that over 30 percent of the costs incurred in developing driving attributes could be saved by frontloading the engineering activity on a DIL simulator with subjective feedback.
Using Vehicle Dynamics capable Simulators for RDE
Historically, driving simulators have reacted too slowly to driver input to deliver the realism necessary to trigger driver behaviour that is fully representative of ‘real world’ driving. rFpro’s software provides unprecedented realism when used with the latest generation of lighter motion platforms with faster responses. It does this through the use of lag-free high resolution graphics and finely detailed road surface models faithfully recreating cambers, gradients, bumps and potholes.
rFpro customers have linked their driving simulators to both engine and drivetrain dynos, enabling highly repeatable tests to be conducted with a human driver in control. This allows vehicle manufacturers to identify and isolate those aspects of human driving which differ significantly from computer controlled operation, leading to improved accuracy in predicting how the vehicle will ultimately perform in an RDE test. The low variance between results allows statistical methods to be applied and saves time and cost compared to real car testing on public roads or at test facilities.