Total cost of ownership optimization model for battery-electric trucks
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Typ
Examensarbete för masterexamen
Program
Management and economics of innovation (MPMEI), MSc
Publicerad
2022
Författare
Geromel Dotto, Henrique
Thor Magnusson, Kristinn
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
As electric trucks are relatively new to the market, buyers’ purchasing processes
are not used to evaluate electric trucks and manufacturers want to facilitate the
adoption. A total cost of ownership (TCO) is often used to evaluate investments,
thus sellers can promote the most cost efficient electric truck to the customer by
analysing TCO. This thesis seeks to create a TCO optimisation model to select a
truck variant with the lowest TCO based on vehicle routing, energy consumption
and heterogeneous fleet.
To create the TCO model, a literature review and a commercial study were con ducted. In parallel, data was collected from internal meetings, the literature and
desk research. From the data collection, it was concluded that a single model would
not be able to account for different customers’ needs. Three models were built:
a heterogeneous electric vehicle routing problem with time windows (HEVRPTW)
formulated as a mixed integer programming (MIP) model, a hybrid model, com posed of a VRPTW MIP and a spreadsheet, and an ant colony optimization (ACO)
model.
Three realistic cases were built to validate the model, analyse the TCO and the
selection of electric trucks and assess the supply chain impact. Then, a sensitivity
analysis of the TCO is performed. The results show that driver cost and initial cost
have the most impact on TCO, whereas energy cost increases with mileage. The
supply chain is heavily impacted by the battery size as there are trade-offs when
changing the battery capacity. The optimal battery capacity is a combination of the
energy required and the charging strategy.
Beskrivning
Ämne/nyckelord
Total cost of ownership (TCO) , Electric truck , BEV , Optimization , Ant Colony , Vehicle routing , Heterogeneous fleet , Purchasing process