MFC and Battery Consumption Model

April 2022 — Technical note #68

Geline Canayon

Product Specialist at Aimsun

The Microscopic Free-flow aCceleration (MFC) Model accurately and consistently captures the acceleration dynamics of vehicles based on engine parameters, also considering the environmental conditions and driver behavior. This allows for a more accurate calculation of emissions and fuel consumption which are statistics influenced by vehicle dynamics. While the MFC introduces complexity to the calculation, there is minimal impact on the run time of the simulation because it uses pre-built gear and acceleration profiles for the different Euro Car Segments to determine the acceleration of a vehicle. The model includes petrol, diesel and electric engines. In the case of combustion engines, first, the gear is determined from the vehicle’s speed and the driving style. Second, the engine’s acceleration is taken from the acceleration curve based on the gear and speed. Lastly, the vehicle acceleration is calculated, considering the resistances such as the rolling and drag resistance. In the case of electric engines, there is only one speed-acceleration curve to which the resistances are also applied. Moreover, for all engine types, a driving style factor is applied to calculate the current acceleration.

Image 1

When looking at the acceleration for Euro Car Segment A in Image 1, the Gipps Model may tend to overestimate the acceleration at higher speeds and TWOPAS is slightly better but may still be overestimating the acceleration compared to what the MFC Model calculates. It’s not to say that the first two models are not adequate in typical modeling but rather when doing an environmental study, the emissions and fuel consumption statistics can be more precisely computed by using an advanced acceleration model such as the MFC.

The Battery Consumption Model uses the MFC Model to obtain the motor/generator power train of the engine to get the instantaneous battery charging level. It also considers the efficiency of different processes involved in the operation of the vehicle: the motor/generator, regenerative braking, and transmission and collects the effect of the ambient temperature due to the consequent accessory power required for heating/cooling the vehicle’s cabin. First, it determines the engine acceleration based on the vehicle’s speed and second, the vehicle acceleration is calculated considering the resistances.

Inputs of the MFC and Battery Consumption Model

Vehicle

The Vehicle Category is defined by vehicle type and Engine Type is defined by the fleet composition of the vehicle type. Only the Car category is supported at this time.

The Segment is taken from Weight parameter distribution of the vehicle type. The distribution of the Euro Car Segment by Engine Type is detailed in the Aimsun Next User Manual.

The following videos show the vehicle performance under free-flow conditions with the Gipps Model and the MFC Model by Euro Car Segment for petrol and electric vehicles.

Driver Behavior

The Headway Aggressiveness parameter defines the gear-shifting and driving style of the vehicle type. The accepted values are within -1.00 and 1.00.

The following video shows the impact of the headway aggressiveness parameter during the simulation.

Road Conditions

The slope is defined by the segments of a section. This has an impact on the vehicle’s engine resistance.

The weather and temperature are defined in the Scenario’s Parameters tab. The weather is used in the MFC model, and the temperature is used by the Battery Consumption Model. The ambient temperature affects the power required for heating or cooling inside the vehicle.

The following video shows the impact on driver behavior of a sunny versus a snowy day.

Fuel Consumption

The fuel consumption of combustion engine vehicles is defined by vehicle type.

The following video shows the fuel consumption of different Euro Car Segments (with their respective tank capacity) in MFC.

Battery Consumption

The battery consumption of a vehicle type is affected by the Electric Accessories Power required for heating or cooling inside the vehicle.

The following video shows the battery consumption of each Euro Car Segments (with their respective battery capacity) in MFC.

Energy Consumption

For both combustion engine and electric vehicles, the distribution of the initial fuel or battery level in percentage can be modified. This sets the fuel or battery level of vehicles at the beginning of a simulation.

Activating the MFC and Battery Consumption Model

The MFC model must be activated at both the Vehicle Type and Experiment level. Note that the MFC is not compatible with the TWOPAS Model, so the MFC model will not be available as an option if the vehicle type has TWOPAS activated.

Vehicle Type:

Experiment:

Outputs of the MFC and Battery Consumption Model

The Fuel and Battery Consumption statistics are generated for the entire network, each section and turn and for subpaths. View Modes can be used to visualize fuel or battery consumption in the network.

At the vehicle level, the current fuel level or state of charge, current fuel or battery consumed, and total fuel or battery consumed are statistics available in the simulation vehicle’s Dynamic Attributes.

View modes can be used to mark vehicles by engine type or current state of charge as seen in the following examples.

What to keep in mind:

  • The MFC model is not compatible with the TWOPAS model.
  • The MFC is only available for vehicle category Car. It currently does not cover trucks, buses, and motorbikes.
  • The MFC model is only applied when vehicles are under free-flow conditions.
Related papers
  • Introducing Electrified Vehicle Dynamics in Traffic Simulation. He, Y., Makridis, M., Mattas, K., Fontaras, G., Ciuffo, B., & Xu, H. (2020).  Transportation Research Record, 2674(9), 776–791. https://doi.org/10.1177/0361198120931842
  • MFC Free-Flow Model: Introducing Vehicle Dynamics in Microsimulation. Makridis, M., Fontaras, G., Ciuffo, B., & Mattas, K. (2019). Transportation Research Record, 2673(4), 762–777. https://doi.org/10.1177/0361198119838515
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