Background: Climate changes and global warming call for behaviour changes from mankind and for new business models to introduce sustainable innovations. Financial analysis plays an important role in guiding the choice of these business models. However, assumptions and uncertainties pose challenges to the use of financial analysis in business modelling. Purpose: The purpose of this study is to develop a proactive systematic approach of financial analysis in business modelling. Accounting for the important role of assumptions and uncertainty factors, the approach should guide the choices of capital structure, revenue model, and strategic partnerships in the business model. Methodology: The developed approach combines the use of different methods to assess different business models for a public fast-charging stations for electric heavy-duty vehicles in Sweden. The used techniques are DCF analysis, What-If analysis, Tornado diagram, Monte-Carlo simulation, and multiple linear regression analysis. Results and analysis: Applied to the case of a public fast-charging station for electric heavy-duty vehicles, the approach leads to the identification of potential viable business models. Under the condition of using financial leverage through debt, additional revenue sources such as per-charge event user fee and advertising as well as partnership with the public sector in the form of grants, it is possible to achieve a viable business model. Conclusions: A systematic proactive approach of the use of financial analysis in business modelling was successfully developed and applied to the case of fast-charging stations for electric heavy-duty vehicles. The identified viable business models rely on financial leverage through debt, additional revenue sources and partnership with the public sector in the form of grants. Recommendations for future research: Simulations with more input parameters as well as combinations with observational studies of existing business models can be further investigated.

The deployment of electric vehicles has speeded up during the past ten years. As heavy-duty trucks are a significant source of GHG emissions, electrification is an encouraging way to lead to sustainability beyond doubt. However, some constraints regarding electric vehicles have emerged. Range extension is a primary challenge of the development of electric vehicles, where thermal conditioning systems can have a considerable impact. Some researches have been done on electric passenger vehicles. However, studies regarding the energy consumption for the thermal conditioning system of heavy-duty electric vehicles are scarcely provided.This study therefore focuses on estimating the energy consumption for the auxiliary heating/cooling and studying the influence of the ambient temperature, vehicle velocity, payload, and driving cycles. A designed integrated thermal conditioning system model was constructed in GT-SUITE, with three subsystems to provide thermal comfort for the truck cabin, meet the operative temperature for battery packs and condition the power electronics and the electric machine. Calibrations were done and yielded acceptable relative errors less than 10%, regarding the cabin and battery heaters.The study shows that the thermal conditioning system consumes the most energy during extremely cold weather, reaching up to 10 kW when the ambient temperature is lower than -20℃. Moreover, the energy consumption during heating/cooling will increase if the vehicle velocity increases. However, it remains stable during mild weather. Payload has different impacts on the energy consumption for heating and cooling. As higher payload results in higher waste heat from the electric machine and batteries, it alleviates the heating while burdens the cooling. Four different driving cycles were simulated, and the result reveals that despite the cycle with the lowest average speed has the highest energy consumption/km, however has the lowest average power.