Transportation Decarbonization - Individual Vehicle Focus

Summary:

• Focus: modeling transportation for individual vehicles

  • Powertrain design

  • Control 

  • Use of vehicles in a wide range of conditions

  • Entire transportation systems

  • Goal: deploy research results and tools in industry

  • Wide range of transportation types: road, rail, road, etc.

• Mission / Simulations:

  • Better vehicles: 

    • Autonomie (developed over 25 years)

    • Vehicle energy consumption

  • Smarter vehicles: 

    • Road Runner (extension to Autonomie)

    • Multi-vehicle powertrain and driving dynamics

  • Smarter roads: 

    • Existing tools: PTV/VISSIM, Aimsun

    • Traffic flow micro-simulations

  • Smarter travelers: 

    • Polaris

    • Integrated travel demand & mesoscopic traffic flow simulation

• Vehicle models integrated with micro- and meso-scale models of traffic

  • Integration with SUMO and Polaris

  • Models that interact cyclically: energy use affects refueling behavior and thus traffic

  • ML models used to approximate simulation models

• Autonomie: Vehicle Energy consumption, performance, cost

  • Approach: forward looking, effort/flow model

  • Model impact of accelerating on the vehicle attributes and then on its energy use profile

  • Gear shifting, powertrain details, pedal positions, etc.

  • This level of fidelity needed to capture small differences in vehicle design (10-20% difference in efficiency)

  • Autonomie emphasizes:

    • Power train control

    • Integration of all the individual vehicle components

    • Validated turn-key vehicle models: existing vehicles and anticipated future vehicle designs

    • AI Engine

    • HPC support

  • Variants:

    • Autonomie: detailed model of vehicle (20s to model 20 mins of real time)

    • Autonomie Lite: simpler model where components and their parameters can be modified but component/vehicle control cannot be modified (10x faster)

    • Autonomie Express: fully compiled models with very few parameters, typically used together with micro and meso-scale traffic sims, 5000+ predefined vehicle models (10x faster than Lite)

    • Autonomie AI: ML approximation of Autonomie Express models (extremely fast)

  • Projects:

    • Collaborating with US Department of Transportation to forecast evolution of vehicle designs in the coming decade

    • Design of electric trucks to find configurations that will be cost effective

    • Total cost of ownership of passenger cars or freight vehicles in Chicago Metro Area, from which one can infer the optimal power train distribution given the distribution of duty cycles

    • Route planning for electric vehicles to optimize power use and plan charging (key for taxis, trucks)

• Bean model:

  • Techno-economic analysis of advanced vehicle technology

  • Analysis: what should the cost of a battery be for an electric vehicle to be cost-competitive with hybrid for a given duty cycle?

  • How does cost parity depend on wider economic factors (e.g. cost of electricity)?

• Road Runner: CAVs Vehicle control and energy consumption

  • Focus: the impact of connectivity and automation on energy consumption

    • Traffic lights

    • Sensors

    • Vehicle sensors

    • Autonomous driving

  • Observation: Autonomous driving systems are not designed for fuel efficiency

    • Their energy consumption is worse because these algorithms are focused on safety first

    • Can improve efficiency by 5-30% with improved sensors and algorithms

      • E.g. catch green wave of traffic lights if integrated with lights

      • E.g. can communicate with other vehicles to align paths

      • Extensive testing on test tracks and roads

    • Model must account for behavior of human drivers

      • Extracted second-by-second driving behavior of humans

• Argonne’s team 

  • Large multi-disciplinary team (40+)

  • 25+ years of experience