Cloud computing and Google Prediction in future Fords will optimize your daily drive, plan your routes, and save energy in the process. How can it do this?

Imagine this scenario: You get behind the wheel of a plug-in hybrid and turn the ignition key. A computerized voice asks, ‘Good morning, are you going to work?’ Answer ‘yes’ and the PHEV’s powertrain is automatically optimized for the most efficient energy use on this specific trip based on the day, time, route, driving style, and more.

Optimization could include foregoing running on battery power at the start of the trip, instead waiting until reaching a portion of the trip where driving conditions will provide maximum all-electric range. Alternatively, optimization could reserve enough battery capacity for a low emissions-only zone that’s part of the route, like those already found in London, Berlin, and Stockholm.

Ford researchers and engineers are developing the technology needed for this personalized vehicle optimization. This involves combining the power of cloud computing, Google Prediction API, and Ford’s own analysis methodologies to provide intelligent routing, driving, and vehicle operation.

The large amount of computation power and information storage needed to make predictions and optimizations – as well as making vehicles smart enough to independently change how they perform with optimal driveability and fuel efficiency – is beyond the capability of current onboard computers. Cloud computing, one example of emerging ‘car-to-x’ or ‘V2V’ (vehicle-to-vehicle) technology, is currently required. Ford already offers cloud-based services through Ford SYNC found in many new Ford products. To date, it is used for infotainment, navigation, wireless smart phone, and real-time traffic information.

Ford is using Google Prediction API to convert information such as historical driving data, which identifies where a driver has traveled and at what time of day, into real-time predictions such as where a driver is headed at the time of departure. The prediction includes how to optimize driving performance to and from the location. Ford researchers are applying Google Prediction API to more than two years of their own predictive driver behavior research and analysis as part of the ongoing program.

Ford has demonstrated conceptually how Google Prediction API could alter the performance of a plug-in hybrid. Some vehicles are equipped with advanced navigation systems that warn drivers when they’re using fuel wastefully. However, a Google-aided Ford Escape hybrid EV prototype actually adjusts the powertrain, or switches from gas to electric power, by predicting where the vehicle is headed or even how it is being driven.

Besides the Escape hybrid, Ford has included the ability to connect a vehicle to the cloud so the driving experience can be tailored based on variables such as personal tastes and moods of the driver. This is showcased in the Ford Evos Concept that was introduced at the Frankfurt Motor Show.

What’s next? Work is now underway to study the feasibility of incorporating other variables such as driver style and habits into the optimization process.