Hybrid Vehicles have experienced major modifications since the last decade. Smart grid success with combination of renewable energy exclusively depends upon the large-scale penetration of Plug-in Hybrid Electric Vehicles (PHEVs) for a sustainable and carbon-free transportation. Recent technical studies regarding various optimization strategies related to PHEV integrated smart grid; such as control and battery charging, vehicle-to-grid (V2G), unit commitment, charging infrastructures, integration of solar and wind energy and demand management prove that electrification of transportation as a rapidly growing field of research. This work presents a holistic review of all substantial research applying metaheuritics optimization for plug-in-hybrid electric vehicles. A summary on future perspective of metaheuristic algorithms is also provided, covering Cuckoo Search (CS), Harmony Search (HS), Artificial Bee Colony (ABC), etc. with a comprehensive reviews on previously applied methods and their performance for solving different real-world problems in the domain of PHEVs. Moreover, significant shifts towards hybrid and hyper metaheuristics are also highlighted.
On certain integrals international energy agency (lEA), transport, energy and C02-moving towards sustainability, Paris, 2009. http://www.iea.org/newsroomandevents/pressreleases/ 2009/october/name,20274,en.html.
2.
Holtz-EakinD. and SeldenT.M., Stoking the fires? CO2 emissions and economic growth, Journal of Public Economics57(1) (1995), 85-101.
3.
KumarL. and JainS., Electric propulsion system for electric vehicular technology: A review, Renewable and Sustainable Energy Reviews20 (2014), 924-940.
4.
ContestabileM., OfferG.J., SladeR., JaegerF. and ThoennesM., Battery electric vehicles, hydrogen fuel cells and biofuels, which will be the winner?Energy and Environmental Science4(10) (2011), 3754-3772.
5.
DjokicS.Z. and PapicI., Smart grid implementation of demand side management and micro-generation, International Journal of Energy Optimization and Engineering (IJEOE)1(2) (2012), 1-19.
6.
BoussaïdI., LepagnotJ. and SiarryP., A survey on optimization metaheuristics, Information Sciences (2013), 82-117, 237.
7.
HannanM.A., AzidinF.A. and MohamedA., Hybrid electric vehicles and their challenges: A review, Renewable and Sustainable Energy Reviews29(0) (2014), 135-150.
8.
HotaA.R., JuvvanapudiM. and BajpaiP., Issues and solution approaches in PHEV integration to the smart grid, Renewable and Sustainable Energy Reviews30(0) (2014), 217-229.
9.
SenvarO., TuranogluE. and KahramanC., Usage of metaheuristics in engineering: A literature review, Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance (2013), 484-528.
10.
BlumC. and RoliA., Metaheuristics in combinatorial optimization: Overview and conceptual comparison, ACM Computing Surveys (CSUR)35(3) (2003), 268-308.
11.
PeriauxJ., GonzalezF. and LeeD.S.C., Evolutionary methods, in: Evolutionary Optimization and Game Strategies for Advanced Multi-Disciplinary Design (2015), 9-20.
12.
MishraE.A., DasM.N. and PandaT.C., Swarm intelligence optimization: Editorial survey, International Journal of Emerging Technology and Advanced Engineering3(1) (2013), 217-230.
13.
ZangH., ZhangS. and HapeshiK., A review of nature-inspired algorithms, Journal of Bionic Engineering7 (2010), 232-237.
14.
BiswasA., MishraK.K., TiwariS. and MisraA.K., Physics-inspired optimization algorithms: A survey, Journal of Optimization, (2013).
15.
PelikanM., SastryK. and GoldbergD.E., Scalability of the bayesian optimization algorithm, International Journal of Approximate Reasoning31(3) (2002), 221-258.
16.
CutelloV., NicosiaG., RomeoM. and OlivetoP.S., On the convergence of immune algorithms, in: Foundations of Computational Intelligence, 2007 FOCI 2007, IEEE Symposium on (2007), 409-415.
17.
AlipourH., AsaeiB. and FarivarG., Fuzzy logic based power management strategy for plug-in hybrid electric vehicles with parallel configuration, in: Proceedings of the International Conference on Renewable Energies and Power Quality, Santiago De Compostela, Spain (2012), 28-30.
18.
AsadiS. and GeemZ.W., Sustainable building design: A review on recent metaheuristic methods, in: Recent Advances in Swarm Intelligence and Evolutionary Computation (2015), 203-223.
19.
TorresJ., GonzalezR., GimenezA. and LopezJ., Energy management strategy for plug-in hybrid electric vehicles, A comparative study, Applied Energy113 (2014), 816-824.
20.
MunkhammarJ., Distributed photovoltaics, household electricity use and electric vehicle charging: Mathematical modeling and case studies, 2015.
21.
HuttererS. and AffenzellerM., Probabilistic electric vehicle charging optimized with genetic algorithms and a two-stage sampling scheme, International Journal of Energy Optimization and Engineering (IJEOE)2(3) (2013), 1-15.
22.
ShaoS., ZhangT., PipattanasompornM. and RahmanS., Impact of TOU rates on distribution load shapes in a smart grid with PHEV penetration, in: Transmission and Distribution Conference and Exposition, 2010 IEEE PES, IEEE, (2010) 1-6.
23.
XuH., MiaoS., ZhangC. and ShiD., Optimal placement of charging infrastructures for large-scale integration of pure electric vehicles into grid, International Journal of Electrical Power and Energy Systems53 (2013), 159-165.
24.
FarhoodneaM., MohamedA., ShareefH. and ZayandehroodiH., Optimum placement of active power conditioners by a dynamic discrete firefly algorithm to mitigate the negative power quality effects of renewable energy-based generators, International Journal of Electrical Power and Energy Systems61 (2014), 305-317.
25.
FazelpourF., VafaeipourM., RahbariO. and RosenM.A., Intelligent optimization to integrate a plug-in hybrid electric vehicle smart parking lot with renewable energy resources and enhance grid characteristics, Energy Conversion and Management77 (2014), 250-261.
26.
HajimiraghaA.H., CanizaresC.A., FowlerM.W., MoazeniS. and ElkamelA., A robust optimization approach for planning the transition to plug-in hybrid electric vehicles, Power Systems, IEEE Transactions on26(4) (2011), 2264-2274.
27.
BarcoJ., GuerraA., MuñozL. and QuijanoN., Optimal routing and scheduling of charge for electric vehicles: Case study, arXiv Preprint (2013), arXiv:1310.0145.
28.
RahmanI., VasantP.M., SinghB.S.M. and Abdullah-Al-WadudM., Swarm intelligence-based smart energy allocation strategy for charging stations of plug-in hybrid electric vehicles, Mathematical Problems in Engineering (2014). Article ID 620425.
29.
TieS.F. and TanC.W., A review of energy sources and energy management system in electric vehicles, Renewable and Sustainable Energy Reviews20 (2013), 82-102.
30.
ChenZ., MiC.C., XiongR., XuJ. and YouC., Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming, Journal of Power Sources248 (2014), 416-426.
31.
SamantaC., PanigrahiS.P. and PanigrahiB.K., Genetic-based bacteria foraging to optimise energy management of hybrid electric vehicles, Electrical Systems in Transportation, IET4(3) (2014), 53-61.
32.
ZhangY., ZhouM. and LuD., Fuzzy logic energy management strategy for plug-in hybrid electric vehicles, Vehicle Engineering2 (2014), 1-7.
33.
RousseauA., PageritS. and GaoD.W., Plug-in hybrid electric vehicle control strategy parameter optimization, Journal of Asian Electric Vehicles6(2) (2008), 1125-1133.
34.
CaoY., TangS., LiC., ZhangP., TanY., ZhangZ. and LiJ., An optimized EV charging model considering TOU price and SOC curve, Smart Grid, IEEE Transactions on3(1) (2012), 388-393.
35.
WangZ., HuangB., XuY. and LiW., Optimization of series hybrid electric vehicle operational parameters by simulated annealing algorithm, paper presented at the control and automation, ICCA 2007 IEEE International Conference on (2007).
36.
YiT., XinZ. and LiangZ., Fuzzy-genetic control strategy of hybrid electric vehicle, paper resented at the intelligent computation technology and automation 2009, ICICTA'09 Second International Conference on (2009).
37.
RahmanS.A., ZhangN. and ZhuJ., Genetic algorithm for UTS plug-in hybrid electric vehicle parameter optimization, Paper Presented at 13th Asia Pacific Vibration Conference (2009).
38.
FangL., QinS., XuG., LiT. and ZhuK., Simultaneous optimization for hybrid electric vehicle parameters based on multi-objective genetic algorithms, Energies4(3) (2011), 532-544.
39.
XuS., FengD., YanZ., ZhangL., LiN., JingL. and WangJ., Ant-based swarm algorithm for charging coordination of electric vehicles, International Journal of Distributed Sensor Networks (2013).
40.
NingD., JunjiW. and YunZ., Research of peak and valley time period partition approach and TOU price on DSM, Automation of Electric Power Systems23(8) (2001), 9-12.
41.
CrowM.L., Economic scheduling of residential plug-in (hybrid) electric vehicle (PHEV) charging, Energies7(4) (2014), 1876-1898.
42.
TaylorM.D., Joule counting correction for electric vehicles using artificial neural networks, Paper Presented at the Twenty-Eighth AAAI Conference on Artificial Intelligence (2014).
43.
RahmanI., VasantP.M., SinghB.S.M. and Abdullah-Al-WadudM., Optimisation of PHEV/EV charging infrastructures: A review, International Journal of Energy Technology and Policy10(3) (2014), 280-296.
44.
RahmanI., VasantP.M., SinghB.S.M. and Abdullah-Al-WadudM., Intelligent energy allocation strategy for PHEV charging station using gravitational search algorithm, AIP Conference Proceedings1621 (2014), 52-59.
45.
AhmadM., MusirinI. and OthmanM., Optimal charging strategy for plug-in hybrid electric vehicle using evolutionary algorithm, Paper Presented at the Power Engineering and Optimization Conference (PEOCO), 2014 IEEE 8th International (2014).
46.
RoriguesY.R., SouzaM.F.Z., LopesB.I.L., SouzaA.C.Z. and OliveiraD.Q., Recharging process of plug in vehicles by using artificial immune system and tangent vector, in: Electricity Distribution (CIRED 2013), 22nd International Conference and Exhibition on (2013), 1-3.
47.
RoteringN. and IlicM., Optimal charge control of plug-in hybrid electric vehicles in deregulated electricity markets, Power Systems, IEEE Transactions on26(3) (2011), 1021-1029.
48.
BashashS., MouraS.J., FormanJ.C. and FathyH.K., Plug-in hybrid electric vehicle charge pattern optimization for energy cost and battery longevity, Journal of Power Sources196(1) (2011), 541-549.
49.
SundströmO. and BindingC., Optimization methods to plan the charging of electric vehicle fleets, in: Proceedings of the International Conference on Control, Communication and Power Engineering (2010), 28-29.
50.
RahmanI., VasantP., SinghB. and Abdullah-Al-WadudM., Hybrid swarm intelligence-based optimization for charging plug-in hybrid electric vehicle, in: Intelligent Information and Database Systems, NguyenN.T., TrawińskiB. and KosalaR. eds, Vol. 9012, 2015, pp. 22-30.
51.
LunzB., YanZ., GerschlerJ.B. and SauerD.U., Influence of plug-in hybrid electric vehicle charging strategies on charging and battery degradation costs, Energy Policy46 (2012), 511-519.
52.
SojoudiS. and LowS.H., Optimal charging of plug-in hybrid electric vehicles in smart grids, Paper Presented at the Power and Energy Society General Meeting, 2011 IEEE (2011), 1-6.
53.
MurgovskiN., JohannessonL., SjöbergJ. and EgardtB., Component sizing of a plug-in hybrid electric powertrain via convex optimization, Mechatronics22(1) (2012), 106-120.
54.
ZhangZ., ChingT.W., LiuC. and LeeC.H.T., Comparison of chaotic PWM algorithms for electric vehicle motor drives, in: IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society (2012), 4087-4092.
55.
YildizE.T., FarooqiQ., AnwarS., ChenY. and IzadianA., Nonlinear constrained component optimization of a plug-in hybrid electric vehicle powertrain, 2010.
56.
PatilR., AdornatoB. and FilipiZ., Design optimization of a series plug-in hybrid electric vehicle for real-world driving conditions, SAE Technical Paper (2010).
57.
HuangF., SarikprueckP., ChengY. and LeeW.-J., Design optimization of PHEV charging station, Paper Presented at the Industrial and Commercial Power Systems Technical Conference (IandCPS), 2012 IEEE/IAS 48th (2012).
58.
KrenekT., RuthmairM., RaidlG.R. and PlanerM., Applying (hybrid) metaheuristics to fuel consumption optimization of hybrid electric vehicles, in: Applications of Evolutionary Computation (2012), 376-385.
59.
HuF. and ZhaoZ., Optimization of control parameters in parallel hybrid electric vehicles using a hybrid genetic algorithm, in: Vehicle Power and Propulsion Conference (VPPC) (2010), 1-6.
60.
LiY., Optimization of PHEV power split gear ratio to minimize fuel consumption and operation cost, 2013.
61.
PourabdollahM., On optimization of plug-in hybrid electric vehicles, ISSN 1403-266X, Lic thesis, Dep. of Signals and Systems, Chalmers University of Technology, Gothenburg, 2012.
62.
CoyS.P., GoldenB.L., RungerG.C. and WasilE.A., Using experimental design to find effective parameter settings for heuristics, Journal of Heuristics7(1) (2001), 77-97.
63.
Mohamad-SalehJ., SulaimanN. and AbroA., A fitter-population based artificial bee colony (JA-ABC) optimization algorithm, in: Computational Problems in Engineering, Springer US (2014), 153-160.
64.
VoropaiN., GammA.Z., GlazunovaA.M., EtingovP.V., KolosokI.N., KorkinaE.S., KurbatskyV.G., SidorovD.N., SpiryaevV.A., TominN.V., ZaikaR.A. and Bat-UndraalB., Application of meta-heuristic optimization algorithms in electric power systems, in: Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, VasantP., ed., Hershey, PA: Information Science Reference, 2013, pp. 564-615.
65.
VoropaiN. and ZorkaltsevV., Optimization models and methods developed at the energy systems institute, International Journal of Energy Optimization and Engineering (IJEOE)2(4) (2013), 1-15.
66.
CebiS., KahramanC. and KayaI., Soft computing and computational intelligent techniques in the evaluation of emerging energy technologies, in: Innovation in Power, Control, and Optimization: Emerging Energy Technologies, VasantP., BarsoumN. and WebbJ., eds, 2012, pp. 164-197.
67.
DostálP., The use of soft computing in management, in: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications, VasantP., ed., 2014, pp. 294-326.
68.
SilberholzJ., and GoldenB., Comparison of metaheuristics, in: Handbook of Metaheuristics, Springer US, (2010), 625-640.
69.
TaleizadehA.A. and Cárdenas-BarrónL.E., Metaheuristic algorithms for supply chain management problems, in: Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, VasantP., ed., Hershey, PA: Information Science Reference, (2013), 110-135.
70.
G.A.E.N.A.SaidA.M. and El-HorbatyE.S.M., A comparative study of meta-heuristic algorithms for solving quadratic assignment problem, arXiv Preprint ArXiv1407 (2014). 4863.
71.
GanesanT., VasantP. and ElamvazuthiI., Hopfield neural networks approach for design optimization of hybrid power systems with multiple renewable energy sources in a fuzzy environment, Journal of Intelligent and Fuzzy Systems26(5), 2143-2154.
72.
KhanhD.V., VasantP., ElamvazuthiI. and DieuV.N., Optimization of thermo-electric coolers using hybrid genetic algorithm and simulated annealing, Archives of Control Sciences24(2) (2014), 155-176.
73.
YangX.S. and DebS., Cuckoo search via Lévy flights, in: Nature and Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on (2009), 210-214.
74.
YangX.S. and DebS., Engineering optimisation by cuckoo search, International Journal of Mathematical Modelling and Numerical Optimisation1(4) (2010), 330-343.
75.
GeemZ.W., KimJ.H. and LoganathanG.V., A new heuristic optimization algorithm: Harmony search, Simulation76(2) (2001), 60-68.
76.
ManjarresD., Landa-TorresI., Gil-LopezS., Del SerJ., BilbaoM.N., Salcedo-SanzS. and GeemZ.W., A survey on applications of the harmony search algorithm, Engineering Applications of Artificial Intelligence26(8) (2013), 1818-1831.
77.
AkinA. and SakaM.P., Harmony search algorithm based optimum detailed design of reinforced concrete plane frames subject to ACI 318-05 provisions, Computers and Structures147 (2015), 79-95.
78.
KongX., GaoL., OuyangH. and LiS., A simplified binary harmony search algorithm for large scale 0-1 knapsack problems, Expert Systems with Applications42(12) (2015), 5337-5355.
79.
KarabogaD. and BasturkB., On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing8(1) (2008), 687-697.
80.
KarabogaD. and AkayB., A comparative study of artificial bee colony algorithm, Applied Mathematics and Computation214(1) (2009), 108-132.
81.
DuttaS., RoyP.K. and NandiD., Artificial bee colony optimization for optimal reactive power dispatch incorporating FACTS devices, International Journal of Energy Optimization and Engineering (IJEOE)3(2) (2014), 38-58.
82.
RahmanI., VasantP.M., SinghB.S.M. and Abdullah-Al-WadudM., Swarm intelligence based state-of-charge optimization for charging plug-in hybrid electric vehicles, Energy and Sustainability V: Special Contributions206 (2015), 261-271.
83.
ShaikhP.H., NorN.M., NallagowndenP. and ElamvazuthiI., Intelligent optimized control system for energy and comfort management in efficient and sustainable buildings, Procedia Technology11 (2013), 99-106.
84.
KleyF., LerchC. and DallingerD., New business models for electric cars - A holistic approach, Energy Policy39(6) (2011), 3392-3403.
85.
BlumC., PuchingerJ., RaidlG.R. and RoliA., Hybrid metaheuristics in combinatorial optimization: A survey, Applied Soft Computing11(6) (2011), 4135-4151.
86.
García-VilloriaA., CorominasA. and PastorR., Pure and hybrid metaheuristics for the response time variability problem, in: Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, VasantP., ed., Hershey, PA: Information Science Reference, 2013, pp. 275-311.
87.
TaleizadehA.A. and Cárdenas-BarrónL.E., Hybrid metaheuristics algorithms for inventory management problems, in: Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, VasantP., ed., Hershey, PA: Information Science Reference, 2013, pp. 312-356..
88.
RaidlG.R., A unified view on hybrid metaheuristics, in: Hybrid Metaheuristics, Springer Berlin Heidelberg, (2006), 1-12.
89.
HadjN.B., KammounJ.K. and NejiR., Application of evolutionary algorithm for triobjective optimization: Electric vehicle, International Journal of Energy Optimization and Engineering (IJEOE)3(3) (2014), 1-19.
90.
GhedjatiF. and KhalouliS., A hybrid meta-heuristic to solve a multi-criteria HFS problem, in: Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, VasantP., ed., Hershey, PA: Information Science Reference, 2013, pp. 252-274.
91.
MinzuV. and BeldimanL., Some aspects concerning the implementation of a parallel hybrid metaheuristic, Engineering Applications of Artificial Intelligence20(7) (2007), 993-999.
92.
CrainicT.G. and ToulouseM., Parallel strategies for meta-heuristics, Springer US, 2003, 475-513.
93.
BuenoP.M., JinoM. and WongW.E., Diversity oriented test data generation using metaheuristic search techniques, Information Sciences259 (2014), 490-509.
94.
FocacciF., LaburtheF. and LodiA., Local search and constraint programming, in: Handbook of Metaheuristics, Springer US, (2003), 369-403.
95.
PurnomoH.D. and WeeH., Metaheuristics methods for configuration of assembly lines: A survey, in: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications, VasantP., ed., 2014, pp. 165-199.
96.
SunG. and ZhangA., A hybrid genetic algorithm and gravitational search algorithm for image segmentation using multilevel thresholding, in: Pattern Recognition and Image Analysis, Springer Berlin Heidelberg, (2013), 707-714.
97.
BurkeE.K., CowlingP.I. and KeuthenR., Effective heuristic and metaheuristic approaches to optimize component placement in printed circuit board assembly, in: Evolutionary Computation, 2000 Proceedings of the 2000 Congress on, IEEE 1 (2000), 301-308.
98.
BurkeE.K., GendreauM., HydeM., KendallG., OchoaG., ÖzcanE. and QuR., Hyper-heuristics: A survey of the state of the art, Journal of the Operational Research Society64(12) (2013), 1695-1724.
99.
M\i s\i rM., VerbeeckK., De CausmaeckerP. and BergheG.V., An intelligent hyper-heuristic framework for chesc 2011, in: Learning and Intelligent OptimizatioN, Springer Berlin Heidelberg, (2012), 461-466.
100.
RenZ., JiangH., XuanJ. and LuoZ., Hyper-heuristics with low level parameter adaptation, Evolutionary Computation20(2) (2012), 189-227.
101.
BilginB., ÖzcanE. and KorkmazE.E., An experimental study on hyper-heuristics and exam timetabling, in: Practice and Theory of Automated Timetabling VI, Springer Berlin Heidelberg, (2007), 394-412.
102.
repinšekM., LiuS.H. and MernikM., Replication and comparison of computational experiments in applied evolutionary computing: Common pitfalls and guidelines to avoid them, Applied Soft Computing19 (2014), 161-170.
