For each trip taken by each vehicle, V2G-Sim runs a vehicle powertrain model that predicts energy consumption, fuel consumption and/or battery state-of-charge (SOC) for any vehicle type on a second-by-second basis, including EVs, PHEVs, HEVs, and conventional vehicles. Users can choose between detailed, intermediate, or simplified powertrain models based on their specific applications and research objectives. The chosen model type is automatically initialized and executed by V2G-Sim for a given vehicle on a given trip. For PEVs, the SOC predictions from these powertrain models are critical for predicting how and how much a vehicle must charge once it plugs into the electricity grid, or how much of a given grid service the vehicle can offer back to the electricity grid, or how the vehicle battery will degrade over time.
The detailed models consider the dynamics of individual powertrain and vehicle components. Users can apply their own vehicle models created in the powertrain modeling software Autonomie, or their own custom vehicle models developed in Matlab or Simulink. For EVs, these detailed powertrain models consider the dynamics and operating regimes of the vehicle’s batteries, traction motor, and propulsion and braking control
system. In predicting energy consumption and SOC during an individual vehicle’s trip, drive line losses and aerodynamic losses are considering, and also losses to ancillary devices such as a vehicle’s HVAC system, audio system, etc. For a PHEV, engine dynamics are also considered including a vehicle’s powertrain control strategy dictating the transitions between charge depleting and charge sustaining operation. Users can calibrate these powertrain models for any vehicle size, weight, powertrain architecture, individual component type, powertrain control strategy, etc. Additionally, the detailed powertrain models enable specification of any drive cycle, trip terrain, or ambient conditions. Using the detailed powertrain models, the second-by-second SOC, C-rate, and temperature of a vehicle battery can be predicted to enable detailed electrochemical studies or battery degradation studies when users activate the appropriate additional sub-models. The built-in detailed powertrain models are validated against experimental measurements of vehicles on chassis dynamometers.
The detailed powertrain models can be run for many vehicles using only a laptop or desktop computer. However, when users seek predictions for vehicle-grid interactions for large numbers of vehicles (e.g. >1000) in a short amount of time, reduced models with simple or intermediate detail can be executed. These reduced models can be automatically calibrated against the detailed models. Running on a laptop computer using a single CPU core, these reduced models enable rapid execution to predict the second-by-second resolved vehicle-grid interactions for 100,000+ vehicles over 24-hours in roughly 1 hour.