Algorithm Helps Drivers Make More Efficient Decisions

Funding offered by the US Department of Energy (DOE) for a Southwest Research Institute project has established an average of 15% energy savings when vehicles equipped with linked and automated vehicle systems, or CAVs, are initiated into traffic.

Algorithm Helps Drivers Make More Efficient Decisions

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CAVs make use of wireless smart technology to interact with other traffic and CAVs infrastructure. SwRI’s eco-driving framework makes use of custom software and predictive powertrain algorithms to allow human drivers to make highly efficient driving decisions utilizing vehicle-to-infrastructure (V2I) vehicle-to-vehicle (V2V), and vehicle-to-everything (V2X) connectivity and communications.

SwRI was given a $3.2 million award from the DOE’s Energy Efficient Mobility Systems (EEMS) initiative to study regarding the energy effect of possible smart infrastructure solutions on overall traffic.

The project made use of real-life traffic data, specialized testing equipment, and computer modeling to measure the advantages of integrating SwRI’s eco-driving framework into various types of vehicles, thereby learning how those vehicles impacted traffic flow.

Understanding how introducing connected and automated vehicles can improve the efficiency of roadways is a growing interest to government and industry alike, especially if they can help reduce energy and emissions output.

Stas Gankov, Senior Research Engineer, Powertrain Engineering Division, Southwest Research Institute

Gankov added, “We wanted to find the right technology approach to produce at least a 15% savings in energy consumption without negatively impacting traffic flow and trip time. The average savings are propagated to both connected and non-connected vehicles.”

Several years have been spent by the Institute in developing advanced CAV technologies to assist passenger vehicles function more efficiently while decreasing carbon emissions and energy consumption.

The predictive eco-driving algorithm developed by SwRI uses information from neighboring vehicles to reduce accelerations. SwRI’s power-split optimization application utilizes knowledge of speeds and routes to improve battery and engine operations to fulfill power demands more efficiently.

The SwRI group assessed the average energy consumption of several passenger vehicles from various manufacturers. The vehicles consisted of altering levels of automation and connectivity with powertrains varying from traditional combustion engines to completely electric.

The scientists assessed the entire traffic energy efficiency of eco-driving-enabled vehicles along with dynamometer testing, automated vehicles driving on a test track, and computer models that have been simulated with various traffic corridors.

We discovered that as eco-driving-enabled vehicles more efficiently drove, using less fuel and overall operating more efficiently, the other vehicles around them adapt and consume less energy, too.

Stas Gankov, Senior Research Engineer, Powertrain Engineering Division, Southwest Research Institute

Gankov added, “As we introduce more CAVs into traffic, we see that the roadway efficiency improves enough to, under the right conditions, reduce overall energy consumption by 15% without affecting trip time and traffic flow. Traffic does not slow but flows in a more optimized manner.”

The EEMS project was constructed on SwRI’s Phase I contributions to DOE’s ARPA-E NEXTCAR (NEXT-Generation Energy Technologies for Connected and Automated On-Road Vehicles) Program, which demonstrated up to 20% energy savings in a test car by integrating vehicle connectivity technology with easy automated powertrain control algorithms. At present, SwRI’s NEXTCAR project is in its second phase thereby making a target to achieve a 30% decrease in energy consumption.


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