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A Model Predictive Approach to Improving Fuel Economy in Urban Driving


A. Köberl

Master Thesis, HS 10 (10015)

A method to improve fuel economy of a passenger vehicle in urban driving by application of model predictive control is presented in this work. Information about urban traffic, such as traffic light timings and traffic speed, is integrated in order to predict future traffic situations and minimize fuel consumption on a given route. Various models to estimate the vehicle’s fuel consumption are evaluated and discussed. Traffic lights are modeled and parameters based on real data are used. A model predictive control problem is formulated by defining a quadratic cost function that captures fuel consumption and by implementing constraints. These constraints impose bounds on the vehicle’s acceleration and velocity, respectively, and depend on the current traffic situation. Software for solving mixed-integer programs is used to generate the optimal solution. Different driving scenarios – with and without traffic – are investigated and simulated. Results show that huge improvements in the vehicle’s fuel efficiency are possible and – at the same time – travel time can be reduced substantially with the proposed method. i


Type of Publication:

(12)Diploma/Master Thesis

M. Morari

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2011:IFA_4114
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