Integrated Power and Thermal Management for Connected and Automated Vehicles (iPTM-CAV) through Real-Time Adaptation and Optimization

Critical Need:

Modern drivers are skilled at anticipating and reacting to the behavior of nearby vehicles and the environment in order to travel safely. Nevertheless, all drivers operate with an information gap - a level of uncertainty that limits vehicle energy efficiency. For instance, safe driving demands that drivers leave appropriate space between vehicles and cautiously approach intersections, because one can never fully know the intentions of nearby vehicles or yet unseen traffic conditions. Closing this information gap can enable vehicles to operate in more energy efficient ways. The increased development of connected and automated vehicle systems, currently used mostly for safety and driver convenience, presents new opportunities to improve the energy efficiency of individual vehicles. Onboard sensing and external connectivity using Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X) technologies will allow a vehicle to "know" its future operating environment with some degree of certainty, greatly narrowing previous information gaps. By providing the ability to predict driving conditions, these technologies could operate the vehicle powertrain (including the engine, transmission, and other components) more intelligently, generating significant vehicle energy savings.

Project Innovation + Advantages:

The University of Michigan is developing an integrated power and thermal management system for connected and automated vehicles (iPTM-CAV), with the goal of achieving a 20% improvement in energy consumption. This increase arises from predicting the traffic environment with transportation analytics, optimizing vehicle speed and load profiles with vehicle-to-everything (V2X) communication, coordinating power and thermal control systems with intelligent algorithms, and optimizing powertrain operation in real time. The additional information made available by V2X and new sensors provides a look-ahead preview of traffic conditions unavailable in vehicles without connectivity. This information can be used to enable intelligent decision-making at multiple levels in powertrain and vehicle control. Key to this project is the team's approach for managing vehicle heat loads and thermal management. Thermal loads have to be properly managed, as they affect multiple vehicle attributes including energy consumption, emissions, safety, passenger comfort, etc. Compared to power delivery, thermal loads cannot be served instantaneously - they take more time to respond to changes, making their prediction much more important. The team's proposed technology includes four solutions: managing and optimizing propulsive power and auxiliary thermal load, predictive thermal management of connected and automated vehicles, optimizing powertrain and exhaust aftertreatment systems by anticipating future conditions, and integrating powertrain and vehicle thermal management systems. The proposed strategies are applicable for a range of vehicles powered by internal combustion engines, hybrid-electric, plug-in hybrid-electric, and all-electric powertrains.

Potential Impact:

The University of Michigan's project enables at least an additional 20% reduction in energy consumption of future connected and automated vehicles.

  • Security: These innovations could lead to a dramatically more efficient domestic vehicle fleet, lessening U.S. dependence on imported oil.
  • Environment: Greater efficiency in transportation can help reduce sector emissions, helping improve urban air quality and decreasing the sector's carbon footprint.
  • Economy: Innovations would further solidify the United States' status as a global leader in connected and automated vehicle technology, while a more efficient vehicle fleet would reduce energy cost per mile driven and bolster economic competitiveness.

Project Progress:

A list of publications is provided here, and a summary of project progress is given in the following poster.


  • San Diego State University
  • Pacific Northwest National Laboratory

Copyright © 2013 Real-time Adaptive Control Engineering Lab at the University of Michigan.