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Details of Grant 

EPSRC Reference: EP/K034472/1
Title: Robot Navigation, Perception and Planning for Intelligent Energy Management in Electric Vehicles
Principal Investigator: Posner, Professor I
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Engineering Science
Organisation: University of Oxford
Scheme: First Grant - Revised 2009
Starts: 31 March 2014 Ends: 30 September 2015 Value (£): 99,191
EPSRC Research Topic Classifications:
Electric Motor & Drive Systems Robotics & Autonomy
Transport Ops & Management
EPSRC Industrial Sector Classifications:
Transport Systems and Vehicles
Related Grants:
Panel History:
Panel DatePanel NameOutcome
07 May 2013 Engineering Prioritisation Meeting 7/8 May 2013 Announced
Summary on Grant Application Form
By 2020 independent forecasts predict hundreds of thousands of plug-in electric and hybrid vehicles on UK roads. While the adoption of this technology is currently driven by environmental concerns, the significant potential of electric and hybrid vehicle technology for sustainable economic growth is becoming increasingly apparent. However, in order for this technology to achieve the penetration required to become a viable mass-market alternative to conventional cars it needs to be perceived as meeting consumers' needs. Recent studies have shown that this mass-market penetration is primarily impeded for all-electric vehicles by fears over range limitations as well as vehicle cost. 'Range Anxiety' is fueled by inaccurate feedback from the vehicle regarding the remaining range available. Costs are driven up primarily by limitations on battery capacity and life, both of which are affected by the number and ferocity of charging cycles.

It is an established fact that the range of an electric vehicle, and therefore the eventual need for charging, is significantly influenced by a number of factors such as the velocity profile and geography along the vehicle's trajectory, the condition of the road or the weather. Repeatedly accelerating up a hill in a traffic jam, for example, is more load-intensive than cruising at constant speed on level ground. However, few of these insights improve the experience of the individual end-user: neither driver-specific information such as driving behaviour or commonly driven routes nor route-specific information such as traffic volume, speed limits or the location of stop-signs and traffic lights are currently exploited when considering vehicle range or battery longevity in every-day deployment.

This project addresses these shortcomings by leveraging state-of-the-art Robotics and Machine Learning techniques for the prediction of vehicle range as well as the optimisation of battery longevity. Methods established in the context of robot navigation and perception are ideally suited to provide evolving, in-situ information on driver behaviour and route infrastructure. In concert with such a driver-specific usage profile of a car, core robotics technologies concerning robust planning and decision making can address the task of deciding when and how long for to charge a vehicle such that battery life is preserved and charging costs are minimised. Therefore, by considering how, where and when a vehicle is traveling this project will lead to improved forecasts of vehicle range as well as to more germane charging regimes.

Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.ox.ac.uk