Abstract Summary
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.
