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The Nature and Strategy of Minimizing the Total Travel Time for Long-Distance Driving of an EV

2024; Institute of Electrical and Electronics Engineers; Linguagem: Inglês

10.1109/tte.2024.3365009

2577-4212

Junzhe Shi, Teng Zeng, Scott Moura,

Autonomous Vehicle Technology and Safety

The famous Cannonball Run, a cross-country driving challenge from New York City to Los Angeles, highlights the unique challenges of long-distance electric vehicle (EV) route planning. The time record for an internal combustion vehicle is 25 hours, 39 minutes. Compare this to the EV record of 42 hours, 17 minutes, achieved with a Tesla Model S, which elucidates the complexities inherent to optimal EV route planning. To bridge this divide, our study introduces a system designed for real-time vehicle-to-cloud (V2C) interaction aimed at enhancing online long-distance EV route planning. Our approach integrates four pivotal components: (i) a real-time route data processing module, (ii) an energy consumption module that works for different road conditions, (iii) an EV charge time prediction module grounded on real EV charging data, and (iv) a comprehensive optimization module using a Mixed-Integer Linear Programming (MILP). In applying this system to the Cannonball Challenge, our simulation results surpass the real-world EV time record. Importantly, our integrated system's potential extends beyond this challenge, offering robust solutions for personal and commercial EV long-distance drives.