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Thesis Work: Impact of actuator dynamics on optimal energy management of a Hybrid Electrical Vehicle
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 beneficial. This depends both on the vehicle and on the driving mission ahead of the vehicle.
A common approach for real-time optimal energy management is to view the problem as a series of stationary optimization problems. This approach neglects the transition cost from one operating point to another. It also makes it hard to in an optimization context include discrete decisions, such as gear shift and engine starts, which is instead often included in a heuristic way.
Modern cars are equipped with a growing set of sensors assessing the environment, such as navigation systems, radar, camera, and even cloud connections. This information increases the possibility of predicting the driving demand along the driving mission, which then should be included in the energy management strategy.
Scope
The aim of this master thesis is to develop and implement an energy management strategy incorporating short term predictions and actuator dynamics. The energy management strategy should then be evaluated versus a strategy where the dynamics are neglected, i.e. when solved as series of stationary optimization problems. The impact of prediction horizon length should also be evaluated.
Profile
Duration
Application
25-03-2024
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