Hierarchical Optimization for Charge Planning and Thermal Management of Battery Electric Trucks
| dc.contributor.author | Borghed, Isac | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för elektroteknik | sv |
| dc.contributor.examiner | Murgovcki, Nikolce | |
| dc.contributor.supervisor | Lindgärde, Olof | |
| dc.contributor.supervisor | Suman, Saurabh | |
| dc.date.accessioned | 2024-06-20T08:17:40Z | |
| dc.date.available | 2024-06-20T08:17:40Z | |
| dc.date.issued | ||
| dc.date.submitted | ||
| dc.description.abstract | Abstract To meet the climate goals set by the UN and the EU the shift from vehicles driven by fossil fuels to zero- and low-emission vehicles (ZLEV) needs to be accelerated. One such focus area is the adoption of battery-electric vehicles (BEVs). At the moment, electric trucks suffer from a range disadvantage compared to internal combustion engine (ICE) trucks, and since the recharging times are long the optimal planning of charging becomes a complex logistical problem. In this thesis the problem of optimal charge planning and thermal management for a specific route and speed profile is studied, where the objective of the optimization is to minimize the total cost of operation (TCOP), taking into consideration stochastic uncertainties such as traffic speed and charging station queuing times. The optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is solved by decomposing the problem into two layers. The top layer is solved using stochastic dynamic programming (SDP) for a simplified version of the MINLP. The charging decisions found using SDP are sent to the lower layer which can then be formulated as a nonlinear programming (NLP) problem which is solved using the FORCESPRO NLP solver using a more comprehensive model of the truck’s dynamics. The mission planner is evaluated on a 460 km route and a 660 km route, with both including recharging stations. It is found that the hierarchical mission manager reduces costs by up to 7.8% compared to a previously developed mission manager which simplified the MINLP to an NLP using a sigmoid activation function to represent charging decisions. The hierarchical solver has also been found to improve the solution’s robustness and can consider time-varying disturbances such as traffic speed and charging prices. | |
| dc.identifier.coursecode | EENX30 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/307957 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | Technology | |
| dc.subject | Keywords: Mixed integer nonlinear optimization, Stochastic optimal control, Battery electric vehicles. | |
| dc.title | Hierarchical Optimization for Charge Planning and Thermal Management of Battery Electric Trucks | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Systems, control and mechatronics (MPSYS), MSc |