TY  - BOOK
PB  - IGI Global
SP  - 374
AV  - none
ID  - scholars5960
N1  - cited By 20
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954245094&doi=10.4018%2f978-1-4666-8291-7.ch012&partnerID=40&md5=636afcf8ff60030352f0598ed2afba91
A1  - Rahman, I.
A1  - Vasant, P.
A1  - Singh, B.S.M.
A1  - Abdullah-Al-Wadud, M.
EP  - 405
N2  - In this chapter, Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) technique were applied for intelligent allocation of energy to the Plug-in Hybrid Electric Vehicles (PHEVs). Considering constraints such as energy price, remaining battery capacity, and remaining charging time, they optimized the State-of-Charge (SoC), a key performance indicator in hybrid electric vehicle for the betterment of charging infrastructure. Simulation results obtained for maximizing the highly nonlinear objective function evaluates the performance of both techniques in terms of global best fitness and computation time. © 2015, IGI Global. All rights reserved.
SN  - 9781466682924; 1466682914; 9781466682917
KW  - Benchmarking; Charging (batteries); Evolutionary algorithms; Hybrid vehicles; Particle swarm optimization (PSO); Swarm intelligence
KW  -  Battery capacity; Charging infrastructures; Computation time; Gravitational search algorithm (GSA); Key performance indicators; Nonlinear objective functions; Particle swarm optimization technique; Plug-in hybrid electric vehicles
KW  -  Plug-in hybrid vehicles
TI  - Swarm intelligence-based optimization for PHEV charging stations
Y1  - 2015///
ER  -