On Discharge Strategies for Plug-in Hybrid Electric Vehicles
Licentiatavhandling, 2011

During recent years electrification of vehicles has emerged as a promising technology to reduce oil dependency and CO2 emissions within the transportation sector. At the present day several automotive manufacturers are about to introduce plug-in hybrid electric vehicles to the market, i.e. hybrid electric vehicles with high capacity batteries that are grid rechargeable. As with most new technologies this introduces new control problems to be solved by engineers and researchers. This thesis investigates one of these new control problems, namely how to optimally discharge the battery of a plug-in hybrid electric vehicle on trips that exceed the electric range. Rather than using the trivial discharge strategy, which is to operate as an electric vehicle until the battery is depleted and then proceed in charge sustaining operation as a conventional hybrid electric vehicle, it is possible to improve powertrain efficiency if the battery is discharged gradually throughout the trip. A gradual discharge lowers the average discharge current, thereby lowering the resistive losses that are quadratic in current. However, to find a suitable discharge rate some a priori information regarding the future trip is required. In the thesis it is shown that the a priori information needed can be obtained using route recognition; an algorithm with low computational demand is proposed and evaluated using simulations on logged commuter driving data. The results suggest that notable fuel cost reductions are possible for commuters that frequently drive along routes that exceed the electric range. Furthermore, the impact of trip length uncertainty on the optimal discharge rate is also studied and results indicate that it is preferable to underestimate rather than overestimate the trip length. A separate investigation concludes that uncertain estimates of the battery state of charge only has minor effects on the optimal discharge rate.

Route Recognition

Energy Management

Dynamic Programming

Hybrid Electric Vehicles

Plug-in Hybrid Electric Vehicles

Opponent: Professor Lars Nielsen, Linköpings Universitet


Viktor Larsson

Chalmers, Signaler och system, System- och reglerteknik


Hållbar utveckling





R - Department of Signals and Systems, Chalmers University of Technology: R013/2011


Opponent: Professor Lars Nielsen, Linköpings Universitet

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