Plug-in Electric Vehicle Charging Optimization Using Bio-Inspired Computational Intelligence Methods

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.

[1]  Wencong Su,et al.  Performance evaluation of a PHEV parking station using Particle Swarm Optimization , 2011, 2011 IEEE Power and Energy Society General Meeting.

[2]  H. Neumann,et al.  The potential of photovoltaic carports to cover the energy demand of road passenger transport , 2012 .

[3]  Marjan Mernik,et al.  Replication and comparison of computational experiments in applied evolutionary computing: Common pitfalls and guidelines to avoid them , 2014, Appl. Soft Comput..

[4]  Hosam K. Fathy,et al.  Plug-in hybrid electric vehicle charge pattern optimization for energy cost and battery longevity , 2011 .

[5]  M. Hadi Amini,et al.  ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation , 2016 .

[6]  Bo Xing,et al.  Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms , 2013 .

[7]  Jin Wang,et al.  A full study of a PHEV charging facility based on global optimization and real-time simulation , 2011, 8th International Conference on Power Electronics - ECCE Asia.

[8]  Dunbar P. Birnie,et al.  Solar-to-Vehicle (S2V) Systems for Powering Commuters of the Future , 2009 .

[9]  Mariesa L. Crow,et al.  Economic Scheduling of Residential Plug-In (Hybrid) Electric Vehicle (PHEV) Charging , 2014 .

[10]  M. Hadi Amini,et al.  Simultaneous allocation of electric vehicles’ parking lots and distributed renewable resources in smart power distribution networks , 2017 .

[11]  Morten Lind,et al.  Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects , 2016 .

[12]  Zhile Yang,et al.  Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review , 2015 .

[13]  Azah Mohamed,et al.  A review of the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles , 2016 .

[14]  He Jiang,et al.  Hyper-Heuristics with Low Level Parameter Adaptation , 2012, Evolutionary Computation.

[15]  Pandian Vasant,et al.  A comprehensive review on theoretical framework‐based electric vehicle consumer adoption research , 2017 .

[16]  A.M. Foley,et al.  State-of-the-art in electric vehicle charging infrastructure , 2010, 2010 IEEE Vehicle Power and Propulsion Conference.

[17]  M. R. Poursistani,et al.  Smart charging of plug-in electric vehicle using gravitational search algorithm , 2014, 2014 Smart Grid Conference (SGC).

[18]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[19]  Mo-Yuen Chow,et al.  Performance Evaluation of an EDA-Based Large-Scale Plug-In Hybrid Electric Vehicle Charging Algorithm , 2012, IEEE Transactions on Smart Grid.

[20]  George K. Karagiannidis,et al.  Charging Schemes for Plug-In Hybrid Electric Vehicles in Smart Grid: A Survey , 2016, IEEE Access.

[21]  A. Chowdhury,et al.  Cuckoo search algorithm for economic dispatch , 2013 .

[22]  D. Q. Oliveira,et al.  Recharging process of plug in vehicles by using artificial immune system and tangent vector , 2013 .

[23]  Iztok Fister,et al.  Adaptation and Hybridization in Nature-Inspired Algorithms , 2015 .

[24]  Qi Zhang,et al.  Integration of PV power into future low-carbon smart electricity systems with EV and HP in Kansai Area, Japan , 2012 .

[25]  Xue Liu,et al.  Smart Charging for Electric Vehicles: A Survey From the Algorithmic Perspective , 2016, IEEE Communications Surveys & Tutorials.

[26]  Jin-Woo Jung,et al.  Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration , 2014 .

[27]  Ranjit Roy,et al.  Economic analysis of unit commitment with distributed energy resources , 2015 .

[28]  Asheesh K. Singh,et al.  Optimal infrastructure planning of electric vehicle charging stations using hybrid optimization algorithm , 2016, 2016 National Power Systems Conference (NPSC).

[29]  A.A.A. Elgammal,et al.  Self-regulating particle swarm optimised controller for (photovoltaic-fuel cell) battery charging of hybrid electric vehicles , 2012 .

[30]  Pandian Vasant,et al.  On the performance of accelerated particle swarm optimization for charging plug-in hybrid electric vehicles , 2016 .

[31]  Lei Jing,et al.  Ant-Based Swarm Algorithm for Charging Coordination of Electric Vehicles , 2013, Int. J. Distributed Sens. Networks.

[32]  G. Rizzoni,et al.  Effects of different PHEV control strategies on vehicle performance , 2009, 2009 American Control Conference.

[33]  Marc A. Rosen,et al.  Intelligent optimization to integrate a plug-in hybrid electric vehicle smart parking lot with renewable energy resources and enhance grid characteristics , 2014 .

[34]  S. Siva Sathya,et al.  A Survey of Bio inspired Optimization Algorithms , 2012 .

[35]  Marco Sorrentino,et al.  Solar energy for cars: perspectives, opportunities and problems. , 2010 .

[36]  Mohsen Parsa Moghaddam,et al.  Reliability constrained unit commitment with electric vehicle to grid using Hybrid Particle Swarm Optimization and Ant Colony Optimization , 2011, 2011 IEEE Power and Energy Society General Meeting.

[37]  Xin-She Yang,et al.  Swarm Intelligence and Bio-Inspired Computation , 2013 .

[38]  Sarvapali D. Ramchurn,et al.  Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey , 2015, IEEE Transactions on Intelligent Transportation Systems.

[39]  Marjan Mernik,et al.  Exploration and exploitation in evolutionary algorithms: A survey , 2013, CSUR.

[40]  Russell Bent,et al.  Locating PHEV Exchange Stations in V2G , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[41]  Eylem Ekici,et al.  PHEVs charging stations, communications, and control simulation in real time , 2011, 2011 IEEE Vehicle Power and Propulsion Conference.

[42]  Junita Mohamad-Saleh,et al.  A Modified Artificial Bee Colony (JA-ABC) Optimization Algorithm , 2013 .

[43]  Mo-Yuen Chow,et al.  Intelligent energy management system simulator for PHEVs at municipal parking deck in a smart grid environment , 2009, 2009 IEEE Power & Energy Society General Meeting.

[44]  Shahrina Md Nordin,et al.  Adoption of PHEV/EV in Malaysia: A critical review on predicting consumer behaviour , 2017 .

[45]  Adel Nadjaran Toosi,et al.  Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications , 2012, Artificial Intelligence Review.

[46]  Mladen Kezunovic,et al.  PHEVs as dynamically configurable dispersed energy storage for V2B uses in the smart grid , 2010 .

[47]  H. Morais,et al.  Particle Swarm Optimization based approaches to vehicle-to-grid scheduling , 2012, 2012 IEEE Power and Energy Society General Meeting.

[48]  Pandian Vasant,et al.  Intelligent energy allocation strategy for PHEV charging station using gravitational search algorithm , 2014 .

[49]  Pandian Vasant,et al.  Novel metaheuristic optimization strategies for plug-in hybrid electric vehicles: A holistic review , 2016, Intell. Decis. Technol..

[50]  Peng Wang,et al.  Optimized power trading of a PEV charging station with energy storage system , 2012, 2012 10th International Power & Energy Conference (IPEC).

[51]  Giorgio Rizzoni,et al.  Economic and environmental impacts of a PV powered workplace parking garage charging station , 2013 .

[52]  Xin-She Yang,et al.  Hybrid Metaheuristic Algorithms: Past, Present, and Future , 2015, Recent Advances in Swarm Intelligence and Evolutionary Computation.

[53]  M. Abdullah-Al-Wadud,et al.  Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques , 2016 .