Total cost of ownership optimization model for battery-electric trucks
Examensarbete för masterexamen
Geromel Dotto, Henrique
Thor Magnusson, Kristinn
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.
Total cost of ownership (TCO) , Electric truck , BEV , Optimization , Ant Colony , Vehicle routing , Heterogeneous fleet , Purchasing process