2023
DOI: 10.3389/fenrg.2022.996726
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Path planning for EVs based on RA-RRT* model

Abstract: The optimal path planning for EVs (electric vehicles) has gained great attention during the last decade due to the zero pollution emission characteristics and limited power capacity of EV batteries. In this paper, an optimal route search is proposed considering multiple charging stations in a dynamic urban environment, while it is still applicable when the initial available amount of the battery fails to cover a certain travel range. The TRDP (transit route design problem) and TNDP (transit node design problem… Show more

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Cited by 3 publications
(1 citation statement)
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References 30 publications
(24 reference statements)
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“…Furthermore, Ref. [23] proposes optimal route finding considering multiple CSs in a dynamic urban environment, but it is also applicable when the initially available battery capacity does not cover a given range. The Transit Route Design Problem (TRDP) and Transit Node Design Problem (TNDP) are used to search for the most feasible routes based on time and driving range via the improved Route-Assisted Rapid Random Tree (RA-RRT*) algorithm (a probabilistic complete global path planning algorithm that randomly samples the search space to obtain path points and finds a feasible path from start point to destination point).…”
Section: Related Workmentioning
confidence: 99%
“…Furthermore, Ref. [23] proposes optimal route finding considering multiple CSs in a dynamic urban environment, but it is also applicable when the initially available battery capacity does not cover a given range. The Transit Route Design Problem (TRDP) and Transit Node Design Problem (TNDP) are used to search for the most feasible routes based on time and driving range via the improved Route-Assisted Rapid Random Tree (RA-RRT*) algorithm (a probabilistic complete global path planning algorithm that randomly samples the search space to obtain path points and finds a feasible path from start point to destination point).…”
Section: Related Workmentioning
confidence: 99%