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Transportation and Power Systems Division

Autonomie Vehicle System Simulation Tool

Plug and Play tool to accelerate the introduction of advanced vehicle technologies into the marketplace

Autonomie is a most powerful and robust system simulation tool for vehicle energy consumption and performance analysis. Developed in collaboration with General Motors, Autonomie is a MATLAB©-based software environment and framework for automotive control-system design, simulation, and analysis. Its application covers energy consumption, performance analysis throughout the entire vehicle development process (such as model-in-the-loop, hardware-in-the-loop, and software-in-the-loop) by leveraging its Plug-and-Play Powertrain and Vehicle Model Architecture and Development Environment.


  • Complete set of turnkey vehicles for wide range of vehicle classes (light-duty to heavy-duty) and powertrain configurations (conventional, start-stop, hybrid electric vehicles, plug-in hybrid electric vehicles, battery electric vehicles, full hybrid electric vehicles)
  • Validated low-level and high-level control algorithms from Argonne’s Advanced Mobility Technology Laboratory dynamometer test data
  • Link/integrate third party tools for:
    • Plant models (such as LMS AMESim, GTPower, SimScape, and Modelica)
    • Economic and environmental models (such as component cost, LCOD, and GREET)
    • Processes (such as optimization, parallel and distributed computing, and V2X )
    • Model and data management
    • Support industrialization” of models, processes and post-processing through current standards (such as Functional Mock-up Interface)
    • Fully customizable: architecture, models, configurations, use cases, post-processing… (All models and control
    • Post-processing capabilities


Autonomie is used to assess the energy consumption and cost of multiple advanced powertrain technologies, including:

  • Impact of component sizing for different powertrain technologies as well as to define the component requirements (such as power and energy) to maximize fuel displacement for a specific application
  • Impact of component technologies (such as advanced transmissions, engine, and batteries)
  • Impact of powertrain configurations (i.e., conventional vs. hybrid electric vehicles vs. plug-in hybrid electric vehicles vs. battery electric vehicles vs. fuel cell hybrid electric vehicles)
  • Impact of vehicle level control based on different approaches, from instantaneous optimization, rule-based optimization, heuristic optimization and route-based control