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Details of Grant 

EPSRC Reference: EP/N022262/1
Title: Green adaptive control for future interconnected vehicles
Principal Investigator: Lot, Professor R
Other Investigators:
Stanton, Professor N Box, Dr S Evangelou, Dr S
Anvari, Professor B
Researcher Co-Investigators:
Project Partners:
Jaguar Land Rover Limited
Department: Faculty of Engineering & the Environment
Organisation: University of Southampton
Scheme: Standard Research
Starts: 01 April 2016 Ends: 31 August 2019 Value (£): 1,668,848
EPSRC Research Topic Classifications:
Control Engineering Energy Efficiency
EPSRC Industrial Sector Classifications:
Energy Transport Systems and Vehicles
Related Grants:
Panel History:
Panel DatePanel NameOutcome
25 Nov 2015 Transport Energy Demand Announced
Summary on Grant Application Form
Vehicle energy management (EM) systems currently concentrate on controlling the drivetrain to deliver the requested power to the wheels optimally from one or more energy sources, depending on the level of hybridisation of the drivetrain. Despite the existence of a vast range of such systems, encompassing rule-based to optimisation-based schemes, a number of challenges remain and opportunities exist to realise the next generation of more efficient EM control. The Green Adaptive Control for Future Interconnected Vehicles project aims to directly address these challenges by developing, implementing and testing EM systems that will now be global (simultaneous optimisation of the drivetrain energy, auxiliary systems energy and driving speed rather than only of the drivetrain energy), predictive (optimisation over a 'look ahead' horizon rather than just based on the instantaneous power demand), and newly adaptive (taking into account driver's preferences, traffic and other environmental conditions). The ultimate goal is to reduce by more than 3-5% the fuel consumption of the future fleet of passengers and light duty vehicles for a range of drivetrain architectures (conventional, electric and hybrid electric) and auxiliary systems (cooling systems, and other). To reach this objective this project will design, implement and demonstrate a new generation of EM together with an Adaptive Cruise Control system, which will automatically drive the vehicle at the most appropriate speed. For this to be effective, we also need to make the drivers aware of the benefits and to make small changes in their driving behaviour. Indeed, substantial reductions in energy consumption can be achieved by making small changes to the behaviour of a large number of drivers. Human factors methods will be used in this research to optimise the design of such new EM control systems.

The proposed EM systems will have three operating modes: Autonomous, Coaching and Manual, which are all based on the same three layers structure. The first one is the Perception layer, which has the purpose of gathering navigation (e.g. route) information, driving information (e.g. the vehicle position, speed and acceleration), information related to the surrounding vehicles, and finally infrastructure conditions (e.g. the state of the next traffic lights series). We will use this information to feed the Decision layer, which is where the intelligence of the system will lay, and which will also be the core of our project. In the Autonomous mode, the system will manage the car in a much smarter way than a human driver by selecting, case by case, the most appropriate vehicle speed and acceleration taking into account all environmental constraints such as road characteristics, desired time to destination and traffic conditions. Once the EM and speed will be optimised, the Action layer will safely drive the vehicle at the most appropriate speed thanks to the Adaptive Cruise Control system. Even if drivers are not always keen to accept such autonomous systems and want to drive according to their personal style, significant fuel reduction may be achieved by using predictive optimisation, in which the system tries to anticipate the future power demand, which is predicted by the system itself according to the information available. Indeed, by selecting the Manual operating mode, the driver behaviour will be predicted by using a mathematical model that will be appositely developed in this project and eventually we will use such prediction to optimise the EM and reduce fuel consumption. Finally, while using the Coaching operating mode, the most appropriate speed will be calculated by the system and then recommended to the driver by using an appropriate haptic (and possibly visual and acoustic) Human Machine Interface, but the driver will maintain the freedom and the responsibility of keeping the preferred speed.
Key Findings
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Potential use in non-academic contexts
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Organisation Website: http://www.soton.ac.uk