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

被引:4
作者
Bailey, Carter [1 ]
Jones, Bridger [1 ]
Clark, Max [1 ]
Buck, Robbie [1 ]
Harper, Mario [1 ]
机构
[1] Utah State Univ, Dept Comp Sci, Logan, UT 84321 USA
来源
2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) | 2022年
关键词
ENERGY-CONSUMPTION; TEMPERATURE; EMISSIONS; IMPACT; MODEL;
D O I
10.1109/ITSC55140.2022.9922244
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:2696 / 2701
页数:6
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