Long Trip Charging Planning of Battery Electric Vehicle Considering Vehicle Waiting Time Forecast at Fast Charging Stations: A Mixed-Integer Dynamic Programming Approach

Battery electric vehicles have shared concerns such as range anxiety and long charging times compared to conventional vehicles. To consider an electric vehicle as a replacement for a conventional vehicle, it is essential to reduce the cost of ownership and encourage widespread adoption without range concerns. It is appropriate that charging planning should be utilized to tackle such problems. It can also be helpful to have information from charging planning services to apprise incoming vehicles for charging to prepare their trip itinerary. For long-distance road trips, choosing appropriate charging stations can be instrumental in achieving the optimization goal. Selecting the right charging station can affect the total trip time. To optimize charging planning, we utilize mixed-integer dynamic programming to achieve the global optimal solution. The information on vehicle waiting time is used and parameterized from open-source US household travel survey information. Probability distributions of daily trips, daily mileage, and probability distribution of arrival vehicle SOC can be derived. Monte Carlo simulation generates the waiting time at the charging station based on the probability distributions and station configuration. The presented optimal charging plan method is validated by multiple test cases. The test cases are designed based on US Alternate Fuel Corridors utilizing interstate highways. For the selected test scenarios up to 9.6% of time saving is observed when there is no waiting time at the charging stations. The validation tests with waiting time encountered at the charging station, time saving of up to 15.1% is achieved with energy level closer to the minimum limit at the end of the trip. This charging planning method significantly enhances the viability of EVs for long-distance travel by addressing core concerns like range anxiety and lengthy trip time.