2018-01-0607 Published 0 3 Apr 2018 © 2018 Argonne National Laboratory.A Modeling Framework for Connectivity and Automation Co-simulation Namdoo Kim, Dominik Karbowski, and Aymeric Rousseau Argonne National Laboratory Citation: Kim, N., Karbowski, D., and Rousseau, A., “A Modeling Framework for Connectivity and Automation Co-simulation,” SAE Technical Paper 2018-01-0607, 2018, doi:10.4271/2018-01-0607. Abstract This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and auto - mation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion. The framework, called RoadRunner, extends the capability of Autonomie, a vehicle energy consumption and performance modeling platform, to simulate the longitudinal movements of one or more user-defined vehicles along a user-defined route.In the first part of the paper, we provide an overview of how the framework is organized. The route attributes (position of traffic lights, grade, etc.) can be automatically extracted from a digital map after origin and destination are provided. The user defines which vehicle models to simulate and in which order. The Simulink model is then automatically generated from the scenario description. In the second part of the paper, we present an example case of a scenario with an eco-approach, using traffic signals that provide their signal phase and timing information to the vehicle. A two-stage control algorithm inspired by the litera - ture is implemented to adjust the vehicle’s velocity while traveling through a signalized corridor with the goal of minimizing fuel consumption. Finally, we present simula - tion results for speed patterns and powertrain operations in typical roads with multiple intersection, comparing the  energy savings with traditional driving of unconnected vehicles. Introduction Connected automated vehicles (CAVs) are able to access and share information wirelessly with each other and with the infrastructure in real time through vehicle- to-vehicle (V2V) and vehicle-to-infrastructure (V2I) commu - nication protocols. The data rich environment developed through the process of V2V and V2I communication can be used to adjust the vehicle’s movements in coordination with other vehicles and traffic control systems and enhance safety, mobility and energy efficiency. In parallel, electrification, light-weighting, and other powertrain technological improve - ments, combined with optimized energy management allow significant reduction in energy consumption, while adding complexity and cost. These two parallel research tracks on energy-efficiency are rarely combined together, potentially leading to inaccurate energy impact estimations and missing the synergies between powertrain operations and vehicle dynamics. On the one hand, research on eco-driving, with or without connectivity, often uses traffic-flow microsimulation, such as VISSIM [ 1], combined with simplified vehicle powertrain and dynamic models that do not take into account recent or predicted powertrain improvements. One example of eco-driving research is intersection eco-approach, i.e. algo - rithms that utilize traffic signal information to reduce energy consumption and emissions. Generally, energy consumption is minimized when sharp vehicle acceleration or braking events are minimized [ 2, 3, 4, 5 ]. In [ 2,3], approximate fuel consumption models