Open AccessElectric vehicle‐routing problem with charging demands and energy consumption
Author(s) -
Shao Sai,
Guan Wei,
Bi Jun
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2017.0008
Subject(s) - dijkstra's algorithm , battery (electricity) , genetic algorithm , energy consumption , electric vehicle , computer science , automotive engineering , routing (electronic design automation) , power (physics) , energy (signal processing) , battery capacity , shortest path problem , engineering , real time computing , mathematical optimization , computer network , electrical engineering , graph , statistics , physics , mathematics , theoretical computer science , quantum mechanics , machine learning
An electric vehicle‐routing problem (EVRP) is developed to settle some operation distribution troubles such as battery energy limitations and difficulties in finding charging stations for electric vehicles (EVs). Meanwhile, in view of realistic traffic conditions and features of EVs, energy consumption with travel speed and cargo load is considered in the EVRP model. Moreover, to avoid the depletion of all battery power and ensure safe operation, EVs with insufficient battery power can be recharged at charging stations many times in transit. In conclusion, a large, realistic case study with the road network of Beijing urban, 100 customers and 30 charging stations is conducted to test the performance of the model and obtain an optimal operation scheme consisted of the routes, charging plan and driving paths. The EVRP model is solved based on the hybrid genetic algorithm to get the routes and charging plan. The dynamic Dijkstra algorithm with some improvements over the classical Dijkstra algorithm is applied to find the driving paths called the most energy efficient paths between any two adjacent visited nodes in the routes.