With the enhancements in vehicle electrification and autonomous vehicles, Traffic systems are also being improved at an accelerated rate to aid the development of improving fuel economy standards. For this to be possible, it is essential that traffic can be accurately modeled and predicted. The existing toolsets are proprietary and expensive and traffic modeling is not a trivial task due to its dependence on various factors such as place, time, and weather. To address these issues, an entirely open-source Software-In-Loop (SIL) fleet-focused traffic modeling toolset has been developed with the ability to take environmental factors with powertrain-in-the-loop into account leveraging Simulation of Urban Mobility (SUMO) and python.
The proposed SIL toolset encompasses the creation of a microscopic traffic distribution which accounts for the usual traffic trends of a typical day. Parameters such as the number of vehicles entering the network and the speed of all the vehicles at a time of a day can be controlled by tunable weather conditions, which is obtained using weather APIs or datafiles. Given a network, origin and destination roads can be defined to create the routes based on shortest distance using default algorithms (like DUAROUTER) in SUMO on a pre-defined real network from Open Street Map or similar mapping standard. In this current study, the ego vehicle has been called into the traffic system after every hour, and information like battery charge, vehicle speed, and time of its trip are logged after every second at different traffic conditions. This developed framework can be used to test electric vehicles or fleets in a typical traffic scenario (which is determined by the weather conditions) and the effects of the traffic on the ego vehicle’s properties like the state of charge of the battery can be studied and modeled. This work can be extended to CAVs by using communication tools like Veins and OMNET++.