This report presents an approach on how to utilize information on future states of traffic lights to reduce the energy consumption and trip time for a Heavy Duty Vehicle. Model Predictive Control is proposed as a solution to handle the optimisation on-line and the concept is tested for various prediction horizons in which information can be received. Further on, it is investigated if the implemented controller is robust enough to execute the same task in a scenario where only the current state is known and future states are predicted. Comparison with a reference vehicle demonstrates improved fuel economy as well as reduced trip time when the information is given. It is shown that the results are improved as the prediction horizon is extended, but converges after 400-500 meters. As the phases of the traffic lights are predicted, fuel economy can be improved, but it comes at a price from being non-robust with drastic braking and increased trip time as predictions might be inaccurate.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-325352 |
| Date | January 2017 |
| Creators | Thorin, Kristoffer |
| Publisher | Uppsala universitet, Avdelningen för systemteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC E, 1654-7616 ; 17005 |
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