Free floating shared electric vehicles have the potential to contribute to a zero-emission urban transport system. In comparison to station based car sharing (one or two-way), the vehicle can be left anywhere within a given area instead of at designated stations. This gives the user more flexibility in driving the car. Downside of free floating car sharing with electric vehicles is that charging stations are needed across the operated area without certainty of where demand is. A lack of charging stations has been the most important reasons for shutting down services [1]. If charging infrastructure is available, car sharing operators can use different strategies for charging their fleet. These include (1) charging the cars as operators, (2) allowing users to charge and (3) incentivizing users to charge. This research is the first to empirically analyse different charging strategies for electric free floating shared vehicles.

The degree of electrification of vehicle powertrains rose significantly in the last years. For fully electric vehicles particularly longer trips are a problem because of limited range, the necessity to charge and consequently higher travel time. Thus, accurate data on energy consumption, driving and charging options and corresponding routing are critical for long trips. This requires innovations that foster the popularity of battery electric vehicles (BEVs). A combined strategy including route, charging and velocity suggestions can enable seamless use of electric vehicles on long distance trips. Based on a mixed-integer nonlinear program (MINLP) a heuristic approach is shown that calculates route and charger options in heterogeneous networks followed by charging amount and prospective velocities in a time-optimal way. For the comparison of the obtained route-charging-velocity plan, a second dynamic programming (DP) approach is shown. A realistic driving scenario serves for method evaluations. The optimal strategy's aim is a minimum travel time, assuming a predefined final state of charge (SOC). The discussion evaluates both approaches concerning computing time and the obtained results, particularly velocity and charging time. The methods' real-time applicability is shown by calculating optimal strategies.