Plug-in electric vehicle charging optimization using bio-inspired computational intelligence methods

Abstract

Plug-in electric vehicle (PEV) has experienced major transformations since the last few decades. The success of smart electric grid with the addition of renewable energy solely depends on the extensive diffusion of PEV for a carbon-free and sustainable transport sector. Current technical studies concerning numerous optimization methods connected to PEV-integrated smart electric grid such as battery charging and control, unit commitment, vehicle-to-grid (V2G), solar and wind energy integration along with demand-side management have proved that vehicle electrification is a fast developing arena of research. Charging optimization of PEV is an emerging field which is gradually being implemented in many charging infrastructures at a global scale. A near-comprehensive understanding of smart charging capability is crucial for large participation of PEV. Only proper charging can ensure PEV users to be free from ‘range anxiety’ and switch into the new revolution of green vehicle with less CO2 emissions. This chapter discusses on the aspects of bio-inspired computational intelligence (CI)-based optimizations for efficient charging of PEVs. A holistic assessment of significant research works using bio-inspired CI techniques for PEV charging is presented. A summary of future optimization techniques is also discussed, covering cuckoo search (CS), artificial fish swarm algorithm (AFSA), artificial bee colony (ABC), etc., with broad reviews on previous applied techniques and their overall performances for solving various practical problems in the domain of PEV charging. Furthermore, noteworthy shifts in the direction of hybrid and multi-objective CI techniques are also highlighted in this chapter.