Cloud connected delivery vehicles: boosting fuel economy using physics-aware spatiotemporal data analytics and real-time powertrain control

Principal Investigator(s):

Will Northrop, Professor, Mechanical Engineering


  • Shashi Shekhar, McKnight Distinguished Professor, Computer Science and Engineering

Project summary:

The University of Minnesota is leading a team to develop technology that will improve the fuel efficiency of delivery vehicles through real-time vehicle dynamic and powertrain control optimization using two-way vehicle-to-cloud (V2C) connectivity. The project will lead to greater than 20 percent fuel economy improvement over a baseline, 2016 E-GEN series hybrid delivery vehicle that is operating as part of the United Parcel Service (UPS) fleet. Large delivery vehicle fleet operators, such as UPS, currently use analytics to assign routes in such a way as to minimize fuel consumption. Algorithms mine historical data collected from vehicles to determine routes before a driver leaves a distribution center. UPS has also invested in E-GEN series electric powertrain vehicles that allow pure electric driving for extended periods of time and use a small, range-extending gasoline engine generator to charge the battery. This allows for routes longer than 550 miles. However, the current UPS routing algorithms do not interact with the vehicle directly to improve the fuel economy in real time. The University of Minnesota's project is integrating the E-GEN vehicles with real time powertrain optimization and two-way V2C connectivity. The vehicle's powertrain controller is pre-programmed at the beginning of a route to optimize efficiency using historical data and known parameters like terrain, weather, and traffic. Powertrain calibration is optimized and downloaded to the vehicle using V2C connectivity in real time during a delivery route, compensating for parameter changes or unpredicted driver behavior. The team's technology may also be commercialized far quicker because UPS, in particular, already uses E-GEN vehicles. Large delivery fleet operators, more broadly, are also heavily invested in data collection for reducing fuel consumption and actively track their vehicles, both of which are factors that could potentially accelerate deployment.

Project details: