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 Companies in the transporting sector are undergoing an important transformation of electrifyingtheir fleets to meet the industry’s climate targets. To meet customer’s requests, keep its marketposition, and to contribute to a sustainable transporting industry, Scania needs to be in frontof the evolution. One aspect of this is to attract customers by providing accurate information anddetecting customer’s opportunities for electrification. Understanding the natural behavior of weatherparameters and their impact on energy consumption is crucial for providing accurate simulations ofhow daily operations would appear with an electric truck. The aim of this thesis is to map weatherparameters impact on energy consumption and to get an understanding of the correlations betweenenergy consumption and dynamic weather data.

ML and deep learning models have undergone training using historical data from operations per-formed by Scania’s Battery Electric Vehicles(BEV). These models have been assessed against eachother to ensure that they are robust and accurate. Utilizing the trained models ability to providereliable consumption predictions based on weather, we can extract information and patterns aboutconsumption derived from customised weather parameters.

The results show several interesting correlations and can quantify the impact of weather parametersunder certain conditions. Temperature is a significant factor that has a negative correlation withenergy consumption while other factors like precipitation and humidity prove less clear results. Byinteracting parameters with each other, some new results were found. For instance, the effect ofhumidity is clarified under certain temperatures. Wind speed also turns out to be an importantfactor with a positive correlation to energy consumption.