Ultimately, the idea is that the algorithm will also learn from the experiences of its brethren in other cars, by arranging for all such systems to share their data online.
Ideally, such a system would be fed its route and destination in advance, to make things easier to calculate.
But Dr Qi and Dr Barth are realists, and know that is unlikely to happen.
If a satnav were invoked, it would be able to pass relevant information on to the algorithm.
But drivers use satnavs only to get them to unfamiliar destinations.
Hence the researchers’ decision to design a system that does not rely on knowing where it is going.
It seems to work—at least, in simulations.
Using live traffic information to mimic journeys in southern California, Dr Qi and Dr Barth compared their algorithm with a basic energy-management system for plug-in hybrids that simply switches to combustion power once the battery is depleted.
As they report in a paper to be published in IEEE Transactions on Intelligent Transportation Systems, their system was 10.7% more efficient than the conventional one.
If the system is aware in advance that a recharging station will be used as part of the trip (which might be arranged by booking one via the vehicle’s information screen) it can spread the use of electric power throughout the journey, to maximum advantage, knowing when the battery will be topped up.
In such situations the average fuel saving was 31.5%.
Dr Qi and his colleagues now hope to work with a carmaker to test the algorithm on real roads.
If all goes well, and their system proves able to cope with the nightmares of commuting in southern California, they will not be left stranded on the hard shoulder.