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Thesis Work: Real-time capable route-based optimal control using limited input data Hybrid Electric Vehicles.
Background
Utilizing the full potential of hybrid electric vehicles puts high requirements on the control strategy. The aim of the control strategy, or energy management, is to ensure that the additional degree of freedoms introduced through hybridisation is used when and how it is most beneficiary. This depends both on the vehicle and on the driving mission ahead of the vehicle.
If the route is completely known an optimal planning of where and how to use electrical energy can be computed. A common assumption is that the relevant energy flows occur in the timescale of seconds, posing a requirement on the velocity profile to be known with a resolution of seconds. Modern navigation systems can output route data such as speed limits and altitude, quantities that do not vary with the same frequency as the actual velocity of the vehicle. This is due to driver intention, traffic variations etc. The computational capability of the on-board ECUs is also limited, meaning that the computations need to be efficient and that a resolution of seconds would be undesirable.
Scope
The aim of this master thesis is to develop and implement an energy management strategy scheduling where and how to use the electrical energy, using information retrievable from a navigation system. The output of the strategy should be a desired electrical energy trajectory over the complete route. The aim is that the designed strategy should be implementable in a real vehicle, placing strict requirements on the efficiency of the computations, both in terms of CPU and RAM. The designed strategy should also be compared with a strategy with full knowledge of the route.
Profile
Duration
Application
28-02-2024
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