Electric Vehicle Autonomy: Realtime Dynamic Route Planning and Range Estimation Software


As electric vehicles (EV) become more dominant for general traditional and autonomous transportation, more accurate range prediction and efficient route planning is necessary to improve adoption and better support EV owners. The EV Path Range Estimator (EVPRE) presented proposes to fill gaps in current route and path planners by providing energy optimal paths that consider real-time external factors and environmental conditions. EVPRE has effective range estimation which considers specific vehicle models and locations. This paper demonstrates development of vehicle models (validated in hardware), energy efficient route generation, and range prediction. Information is visualized through simple isochrones to quickly convey range information for specific situations and vehicle types

See publication:
This publication pertains to:
Systems of Systems
Publication Authors:
  • Bridger Jones
  • Max Clark
  • Robbie Buck
  • Mario Harper
It appeared in:
Peer-reviewed conference proceedings
Deep learning , Training , Computational modeling , Estimation , Transportation , Predictive models , Electric vehicles