Improving the Freight Productivity of a Heavy-Duty, Battery Electric Truck by Intelligent Energy Management

Project summary:

The project team is developing and implementing an intelligent-Energy Management System (i-EMS) with vehicle-to-cloud (V2C) connectivity integrated with physics-aware spatial data analytics (PSDA). The i-EMS rule-based methods use collected vehicle and operations data and calculated parameters from fleet operator partners Murphy Warehouse and HEB as inputs into physics-based adaptive learning algorithms developed in the project to predict and reduce the future energy consumption of the vehicle. The resulting i-EMS will increase the vehicle driving range and lower the operating cost of BEV Class 8 freight movement trucks that drive ~250 miles per day.

The University of Minnesota is developing the i-EMS technology basis in a current DOE ARPA-E NEXTCAR project for range-extended medium-duty delivery vans. Early results have shown a 20 percent energy efficiency improvement. The proposed Volvo i-EMS project will leverage, and expand on, the developed algorithms for battery-electric trucks and improve the energy management performance for the Volvo FE electric Class 8 tractors and regional-haul freight movement application. The i-EMS will be developed for trucks to operate in extreme ambient operating temperatures, cold to hot.

The demonstrator trucks will operate in revenue service in cold (Murphy Warehouse in Minnesota in the winter months) and hot (HEB in Texas in the summer months) weather to validate and tune the i-EMS. Direct-current fast-charging infrastructure for the demonstration will be installed at the fleets' warehouse depots. Researchers will also analyze fleet partners' collected route data to determine optimal spatial locations for on-route charging of multiple BEV trucks using GIS spatial hotspot detection (to support a potential post-project BEV truck fleet expansion).