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Ford Applies Google Prediction API To Optimize Energy Efficiency In Plug-In Electric Vehicles (With Video)

The folks at Ford’s Research and Innovation department are using information collected from two years’ worth of their own driving behavior to anticipate driver destinations and habits.

For those not on the fast-track to computer engineering, an API, or Application Programming Interface, allows programmers to integrate their own programs or data with another application, program, or service. In this case, Ford researchers are sending the aforementioned driving data to the Google Prediction service in the cloud (on the web). Here’s how the technology could work in the real world, according to Ford:

  • After a vehicle owner opts in to use the service, an encrypted driver data usage profile is built based on routes and time of travel. In essence, the system learns key information about how the driver is using the vehicle
  • Upon starting the vehicle, Google Prediction will use historical driving behavior to evaluate given the current time of day and location to develop a prediction of the most likely destination and how to optimize driving performance to and from that location
  • An on-board computer might say, “Good morning, are you going to work?” If the driver is in fact going to work, the response would be, “Yes,” and then an optimized powertrain control strategy would be created for the trip. A predicted route of travel could include an area restricted to electric-only driving. Therefore, the plug-in hybrid could program itself to optimize energy usage over the total distance of the route in order to preserve enough battery power to switch to all-electric mode when traveling within the EV-only zone

“Once the destination is confirmed, the vehicle would have instant access to a variety of real-time information so it can optimize its performance, even against factors that the driver may not be aware of, such as an EV-only zone,” said Ryan McGee, technical expert, Vehicle Controls Architecture and Algorithm Design at Ford Research and Innovation.

From this example, we can glean that this solution can be exceptionally useful in pure electric or electric-assisted vehicles such as the Ford Focus Electric and the forthcoming C-MAX Energi plug-in electric, where conserving power and determining range is of the essence. And since the prediction mechanism requires a large amount of computing power and data to make predictions and optimizations, the use of off-board (cloud) computing is necessary. Coincidentally,MyFord Touch already incorporates certain elements of the cloud through Sirius Travel Link, which provides continuously updated traffic information, current and forecasted weather, gas station locations and prices, live sports scores and schedules, and theater locations with movie listings and times. Further leveraging the cloud provides Ford vehicles, as well as their drivers and passengers, with even more access to real-time information, large-scale computation capacity, and intelligent machine learning algorithms.

The Motrolix Take

It’s no secret that — now more than ever — consumers are beginning to fancy the presence of high-tech tools in their vehicles: whether it’s infotainment, telematics, fuel economy, or safety, technology is the driving force of automotive innovation. Granted, what we see here is a few years away from becoming available on a vehicle from The Blue Oval… but if the very usage of the Google Prediction API to enable this kind of of functionality doesn’t make Ford a technology company and a leader in automotive telematics, we don’t know what does. Rock on Ford’s Research and Innovation labs!

 

Google Prediction (click to expand)

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Ford Developers Look to Use Google Prediction API to Optimize Energy Efficiency; Research Presented at Google I/O

SAN FRANCISCO, May 10, 2011 – Ford researchers are harnessing the power of cloud computing, analytics and Google innovation to identify technologies that could make tomorrow’s vehicles smart enough to independently change how they perform to deliver optimal driveability and fuel efficiency.

Ford researchers are applying Google’s Prediction API to more than two years of their own predictive driver behavior research and analysis. The Google API can convert information such as historical driving data – where a driver has traveled and at what time of day for example – into useful real-time predictions, such as where a driver is headed at the time of departure.

“The Google Prediction API allows us to utilize information that an individual driver creates over time and make that information actionable,” said Ryan McGee, technical expert, Vehicle Controls Architecture and Algorithm Design, Ford Research and Innovation. “Between Google Prediction and our own research, we are discovering ways to make information work for the driver and help deliver optimal vehicle performance.”

How it works
Ford is hoping to use these types of cloud-stored data to enable a vehicle essentially to optimize itself and perform in the best manner determined by a predicted route.

This week, Ford researchers are presenting a conceptual case of how the Google Prediction API could alter the performance of a plug-in hybrid electric vehicle at the 2011 Google I/O developer conference. In this theoretical situation, here’s how the technology could work:

After a vehicle owner opts in to use the service, an encrypted driver data usage profile is built based on routes and time of travel. In essence, the system learns key information about how the driver is using the vehicle

Upon starting the vehicle, Google Prediction will use historical driving behavior to evaluate given the current time of day and location to develop a prediction of the most likely destination and how to optimize driving performance to and from that location
An on-board computer might say, “Good morning, are you going to work?” If the driver is in fact going to work, the response would be, “Yes,” and then an optimized powertrain control strategy would be created for the trip. A predicted route of travel could include an area restricted to electric-only driving. Therefore, the plug-in hybrid could program itself to optimize energy usage over the total distance of the route in order to preserve enough battery power to switch to all-electric mode when traveling within the EV-only zone

“Once the destination is confirmed, the vehicle would have instant access to a variety of real-time information so it can optimize its performance, even against factors that the driver may not be aware of, such as an EV-only zone,” said McGee.

Because of the large amount of computing power necessary to make the predictions and optimizations, an off-board system that connects through the cloud is currently necessary.

What’s next
Knowing that driver behavior and patterns correlate to overall fuel and energy efficiency during the vehicle ownership experience, Ford researchers are committed to increasing their understanding of driver behavior behind the wheel and to developing accurate protocols to predict it.

“Anticipating the driver’s destination is just one way that Ford is investigating predicting driver behavior,” said McGee. “This information can ultimately be used to optimize vehicle performance attributes such as fuel efficiency and driveability.”

The Google Prediction API is one example of a technology that is helping Ford open doors to new predictive possibilities powered by the cloud.

“Ford already offers cloud-based services through Ford SYNC®, but those services thus far have been used for infotainment, navigation and real-time traffic purposes to empower the driver,” said Johannes Kristinsson, system architect, Vehicle Controls Architecture and Algorithm Design, Ford Research and Innovation. “This technology has the potential to empower our vehicles to anticipate the driver’s needs.”

Helping drivers comply with regulations could be among those needs. For example, the French government is considering creating zones that would mandate vehicles have lower emissions. Cities such as London, Berlin, and Stockholm already have such zones. If a vehicle were able to predict exactly when it might be entering such a zone, it could optimize itself in a way to comply with regulations, such as switching the engine to all-electric mode.

Work is now underway to study the feasibility of incorporating other variables such as driver style and habits into the optimization process so Ford can further optimize vehicle control systems, allowing car and driver to work together to maximize energy efficiency.

Integral to this next-step work is personal information security, an issue that is of the utmost importance to Ford. “We realize that the nature of this research includes the use of personal data and location awareness, something we are committed to protecting for our customers in everything we do,” notes Kristinsson. “A key component of this project is looking at how to develop secure personal profiles that will ensure appropriate levels of protection and specific data use only by the driver and the vehicle to deliver the best driving experience.

“It’s about pure customer benefit and creating individualized and optimized experiences – the right one for each person, vehicle and situation.”

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Motrolix Founder with a passion for global automotive business strategy.

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