Novel metaheuristic optimization strategies for plug-in hybrid electric vehicles: A holistic review

Rahman, Imran; Vasant, Pandian; Singh, Balbir Singh Mahinder; Abdullah-Al-Wadud, M.

doi:10.3233/idt-150245

Cited by 14 publications

(11 citation statements)

References 78 publications

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“…From this future research direction, we have come up with swarm intelligence-based methods with comparative study in order to find out the suitable techniques among swarm intelligence-based optimization domain. According to Rahman et al [31], many papers do not document experimental settings in sufficient detail, and hence replication of experiments is almost impossible. We have found the same problems in the research of PSO-based optimization for charging PHEV.…”

Section: Scope Of Studymentioning

confidence: 99%

“…All of the four techniques were run on same computer along with same iterations (100) and total 50 independent runs in order to ensure the fare comparison [31], [53]. The comparisons among applied swarm intelligence-based techniques are given below.…”

Section: Comparisons Among Swarm Intelligence Techniquesmentioning

confidence: 99%

“…trade-off should be taken into con-sideration between the proper convergence and computational time. As APSO is a population-based optimization techniques and the fitness function is non-linear, so the fitness values fluctuates for each iteration [31][32][33][34]. However, the maximum best fitness remains in the range of 450 to 700 and the minimum best fitness remains in the range of 0.5 to 10.…”

Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

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Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

et al. 2016

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Green technologies gain popularity to reduce the pollution and give higher penetration of renewable energy source in the transportation. This research induce that the extensive involvement of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. It is also noticed that daytime charging station are necessary for daily usage of PHEVs due to the limited all-electric-range. Most of the research in the past have been stated that only proper charging control and infrastructure management can assure the larger participation of PHEVs.Therefore, researchers are trying to develop efficient control mechanism for charging infrastructure in order to facilitate upcoming PHEVs penetration in highway. Nevertheless, most of the past researcher already aware with the issue related to intelligent energy management. Yet, these studies could not fill the gap of the problem associated with intelligent energy management and require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. The outcome of this research study provides four optimization techniques that includes Hybrid method within swarm intelligence group for the State-of-Charge (SoC) optimization of PHEVs. The finding of this research simulation results obtained for maximizing the highly nonlinear objective function evaluate the comparative performance of all four techniques in terms of best fitness, convergence speed and computation time. Finally, the hybridization method (PSOGSA) presented in this dissertation uses the advantages of both PSO and GSA optimization and thus produce higher best fitness values. This study evaluates the performance of standard PSO, then Accelerated version of PSO (APSO), GSA algorithm and then Hybrid of PSO and GSA. The hybridization method (PSOGSA) uses the advantages of both PSO and GSA optimization and thus produce higher best fitness values. However, PSOGSA method takes much longer computational time than single methods because of incorporating two single methods in one algorithm. This research study suggests that PSOGSA method is a great promise for SoC optimization but it takes much longer computational time.

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Section: Scope Of Studymentioning

confidence: 99%

Section: Comparisons Among Swarm Intelligence Techniquesmentioning

confidence: 99%

Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

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Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

et al. 2016

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“…110,111 Another study presents hybrid meta-heuristic techniques application for optimal load shedding planning and operation in an islanded distribution network integrated with distributed generation. 110,111 Another study presents hybrid meta-heuristic techniques application for optimal load shedding planning and operation in an islanded distribution network integrated with distributed generation.…”

Section: Hybrid Optimization Approaches For Uvlsmentioning

confidence: 99%

“…Hybrid meta-heuristic algorithms are presented along with their merits and demerits in other studies. 110,111 Another study presents hybrid meta-heuristic techniques application for optimal load shedding planning and operation in an islanded distribution network integrated with distributed generation. 112 To solve the UVLS problem a hybrid PSO and simulated annealing (SA) method were presented in a previous study.…”

Section: Hybrid Optimization Approaches For Uvlsmentioning

confidence: 99%

A critical review of the state‐of‐art schemes for under voltage load shedding

Larik

Mustafa

Aman

2019

Int Trans Electr Energ Syst

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Summary A blackout is usually the result of increasing load beyond the transmission capacity of the power system. One of the main reasons for power blackouts is voltage collapse. To avoid this problem, the proper corrective measures called load shedding is required. In critical and extreme emergencies, under voltage load shedding (UVLS) is performed as a final remedy to avoid a larger scale voltage collapse. Therefore, UVLS is considered state of the art to achieve voltage stability. This review summarizes and updates the important aspects of UVLS; it also provides principle understanding of UVLS, which are critical in planning such defense schemes. Moreover, this article provides a discussion on recent state‐of‐art UVLS schemes applied in various power industries. Additionally, the pros and cons of the conventional and computational intelligence techniques are discussed. It is envisioned that this work will serve as one‐stop information for power system engineers, designers, and researches.

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

Rahman

Mohamad–Saleh

2018

Studies in Systems, Decision and Control

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Novel metaheuristic optimization strategies for plug-in hybrid electric vehicles: A holistic review

Cited by 14 publications

References 78 publications

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

A critical review of the state‐of‐art schemes for under voltage load shedding

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

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