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Route Optimized Energy Management of Plug-in Hybrid Electric Vehicles

Viktor Larsson (Institutionen för signaler och system, Reglerteknik)
Göteborg : Chalmers University of Technology, 2014. ISBN: 978-91-7597-002-8.

Plug-in hybrid electric vehicles have the potential to significantly reduce the oil dependence within the transportation sector. However, there will always be some trips that exceed the electric driving range, meaning that both electric energy and fuel must be used. For such trips the fuel economy is intimately connected with the energy management system and its ability to schedule the use of the battery. The fundamental problem is that the optimal fuel economy can be reached only if the future trip is known a priori. It is therefore desirable to have a system that can perform three principal tasks: i) acquire a prediction of the future trip, ii) given the prediction precompute feedforward information for the real-time level, and iii) at the real-time level identify the optimal operating points in the powertrain. This thesis investigates all three of the mentioned tasks. It is shown that frequently travelled routes can be identified from logged driving data using hierarchical clustering. Based on the historical driving conditions along the route, it is then possible to precompute an optimal strategy that can be used as feedforward information for the real-time level. Two different methods for such a precomputation are investigated, convex optimization and Dynamic Programming. Particular attention is given to the implementation of a computationally efficient Dynamic Programming algorithm. A real-time control strategy that is based on a closed-form minimization of the Hamiltonian is also presented. The strategy is derived for a power- train with two degrees of freedom, and is implemented in a dynamic vehicle model that is used by a vehicle manufacturer. Simulations with a linearly decreasing battery state of charge reference indicate that the fuel economy can be improved with up to 10%, compared to a depleting-sustaining strat- egy. Real-time compatible controller code is also generated and tested in a production vehicle. The vehicle behaviour during a test drive is similar to simulated behaviour

Nyckelord: Plug-in hybrid electric vehicles, Energy management, Dynamic Programming, Pontryagin principle, Convex optimization, Splines, Data clustering

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Denna post skapades 2014-04-08. Senast ändrad 2014-07-07.
CPL Pubid: 196399


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Institutioner (Chalmers)

Institutionen för signaler och system, Reglerteknik (2005-2017)


Hållbar utveckling
Innovation och entreprenörskap (nyttiggörande)

Chalmers infrastruktur

Relaterade publikationer

Inkluderade delarbeten:

Comparing Two Approaches to Precompute Discharge Strategies for Plug-in Hybrid Electric Vehicles

Commuter Route Optimized Energy Management of Hybrid Electric Vehicles

Analytic Solutions to the Dynamic Programming sub-problem in Hybrid Vehicle Energy Management

Cubic Spline Approximations of the Dynamic Programming Cost-to-go in HEV Energy Management Problems


Datum: 2014-05-12
Tid: 13:15
Lokal: HA2
Opponent: Professor Giorgio Rizzoni, Department of Mechanical and Aerospace Engineering Ohio State University, USA

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