Open AccessElectric vehicle modelling and energy‐efficient routing using particle swarm optimisation
Author(s) -
Abousleiman Rami,
Rawashdeh Osamah
Publication year - 2016
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.2014.0177
Subject(s) - dijkstra's algorithm , particle swarm optimization , energy consumption , electric vehicle , energy (signal processing) , mathematical optimization , computer science , electric energy consumption , swarm behaviour , vehicle routing problem , constraint (computer aided design) , engineering , algorithm , routing (electronic design automation) , automotive engineering , simulation , real time computing , electric energy , shortest path problem , mathematics , electrical engineering , embedded system , power (physics) , physics , quantum mechanics , mechanical engineering , graph , statistics , theoretical computer science
Vehicle routing is traditionally based on Dijkstra or Dijkstra‐like algorithms. These algorithms worked well for fossil fuel vehicles. The increase in pollution levels, government regulations, and pressure from environmental groups have caused an increase in electric vehicles (EVs) production and use. EVs are capable of regenerating energy which creates negative weights in search graphs that traditional algorithms are incapable of handling without some modifications. This study presents a model that characterises the energy consumption of an electric vehicle. Most passive and active factors are presented and applied in the formulation. The presented model is verified against 306 kilometres of driven data and proved to have 1.3% absolute error difference between the real vehicle's consumed energy versus the predicted energy consumption as generated by the model. The model is then used with a particle swarm optimisation algorithm to solve the single constraint optimisation problem of finding the most energy efficient route between 2 points on a map. Simulation and real‐world test results demonstrate savings in the energy consumption of the electric vehicle. Results showed more than 9.2% reductions in the energy consumption of the electric vehicle when driven on the developed algorithms’ suggested routes rather than the ones generated by Google Maps and MapQuest.