Researchers at the University of California at Riverside have implemented software-based controls that improve the efficiency of hybrid-electric vehicles by more than 30 percent.

A paper describing the researchers' work has been accepted for publication in IEEE Transactions on Intelligent Transportation Systems.

The work was led by Xuewei Qi, a researcher at the Center for Environmental Research at Technology at UC-Riverside's Bourns College of Engineering, and by Matthew Bart, the UC-Riverside professor of electrical and computer engineering who serves as director of the center.

The software that achieved such startling efficiency improvements is based on "evolutionary" algorithms that simulate processes under which life processes get more efficient over time.In this case, researchers were inspired by biological evolution and the energy savings realized by birds that fly in formation.

Hybrid vehicles typically rely on simple energy management systems to switch between electric and gas operation.The UC researchers discovered that a more complex, software-based approach to switching from gas to electricity and back again frequently was more efficient.

Simple management systems aren't up to the task, according to the researchers. "In reality, drivers may switch routes, traffic can be unpredictable, and road conditions may change, meaning that the EMS must source that information in real-time," Qi said.

The energy management system developed by Qi and his team relies upon wireless info sources such as cellular networks and evolutionary algorithms that model natural phenomena such as biological evolution and the swarming of insects.