For a successful market take-up of plug-in electric vehicles, fast-charging stations along the highway network play a significant role. This paper provides results from a first study on estimating the minimum number of fast-charging stations along the European highway network of selected countries (i.e., France, Germany, the Benelux countries, Switzerland, Austria, Denmark, the Czech Republic, and Poland) and gives an estimate on their future profitability. The combination of a comprehensive dataset of passenger car trips in Europe and an efficient arc-cover-path-cover flow-refueling location model allows generating results for such a comprehensive transnational highway network for the first time. Besides the minimum number of required fast-charging stations which results from the applied flow-refueling location model (FRLM), an estimation of their profitability as well as some country-specific results are also identified. According to these results the operation of fast-charging stations along the highway will be attractive in 2030 because the number of customers per day and their willingness to pay for a charge is high compared to inner-city charging stations. Their location-specific workloads as well as revenues differ significantly and a careful selection of locations is decisive for their economic operation.

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.

This project investigates the viability of using reconditioned batteries, which have lost part of their original capacity while powering electric vehicles (EVs), to minimize the expenses of fast-charging infrastructures under the three charging scenarios where fast-charging mode is likely to be needed the most. The analysis is conducted for the Spanish scenario and considers the retail electricity tariff that best suits the requirements of a FCS. The economic analysis is performed on an annual basis and is tackled with an optimization algorithm, formulated as a mixed-integer linear programming problem and run on MATLAB. The expected lifetime of the ESS, being made of reused automotive cells, is estimated with a semi-empirical approach, using an iterative process and implemented in MATLAB. A sensitivity analysis is conducted on three input parameters that were identified to have a considerable impact on the system design and performance.   Overall, results show that with current figures energy storage integration in FCSs is viable as it effectively reduces the infrastructure expenses in all scenarios. Peak-shaving is identified as the main source of cost savings while demand shifting is not effective at all. The latter is further discussed in the sensitivity analysis and some considerations are elaborated. The most profitable scenario for storage integration is the case of a fast-charger located in a urban environment while, surprisingly, the lowest cost savings are obtained in the highway case. The sensitivity analysis illustrates the impact and effects that electricity prices and specific cost of both the power converter and the second-life batteries produce on the optimal system design. Moreover, charging demand profiles are deeply analyzed and their main implications highlighted.