Smartphone-based accurate range and energy efficient route selection for electric vehicle

摘要

Range anxiety - the fear of running out of battery power while on the road - is one of the major barriers to large scale adoption of Electric Vehicles (EVs). Range prediction solutions are available to address anxiety but most of them have limited functionalities. In this paper we propose a new, “Accurate Range” and “Energy-efficient Route” (ARER) selection mobile software solution which is based on smartphone platform. The proposed solution provides several attractive features. The first and prime feature is estimation of the most accurate driving range considering those real time factors that were never considered in the prior art such as geographical terrain of the driving route (Elevation and Depression), real time alert implemented on the road (i.e. the road flood clogged or blocked due to catastrophe - such information would be received through PLAN (Personal Localized Alerting Network), a new public safety system that FCC and FEMA are working on currently that will enable government officials to send emergency text alerts, such as tornados, floods, terrorisms, to specific affected geographic areas through cell towers in near future), Real Time Wind Speed (tailwind and headwind), real time weight in the EV (onboard Passengers and Cargo), and real time traffic (including not only on road vehicles, but also STOP signs, advisory road signs, and probability of encountering red traffic lights, etc.), comparing with available battery energy. The second key feature that leverages on the first one is proposing the alternate route(s) that may not be essentially shorter but the most energy efficient (e.g. the route with depression instead of elevation and at the same time not flood clogged or blocked, the route with favorable wind direction at that instant and location, the route with lesser traffic congestion, fewer stop signs and fewer red traffic light etc.). The third feature is to evaluate the service relevance and suggest the point- of service; offering similar services, that fall on the most energy efficient route (e.g. if the EV Driver searched for Rite-Aid Pharmacy, the software may also suggest the WalGreens or Wall Mart, or Target, or Shoprite, because of service relevance/similar service offering and occurrence on the most energy efficient route from the EV Driver's current location). The fourth feature is that it keeps the history of the roads traversed and uses the log data for future optimization. Lastly the fifth feature is that it produces a visual 360-degree real time range display, and calculates the estimated energy cost of completing a chosen rout. The software to make prototype for the work is under development.