Imran Rahman

A market modeling review study on predicting Malaysian consumer behavior towards widespread adoption of PHEV/EV

With the rising concern about climate change, there has been an increased public awareness that has resulted in new government policies to support scientific research for mitigating these problems. Malaysia is among the major energy-intense countries and is under an excessive burden to advance its energy efficiency and to also work towards the reduction of its carbon emission. Plug-in hybrid electric vehicles (PHEVs) have the potential to lessen the carbon emission and gasoline consumption in order to alleviate environmental problems. Most of the energy problems linked to the increasing transportation pollution are now being reduced with the solution of the adoption of PHEVs. PHEVs are seen as a solution to cut carbon emission, which prevents environmental damages. Furthermore, PHEVs’ driving range and performance can be comparable to the other hybrid vehicles as well as the conventional IC engines that have gasoline and diesel tanks. Thus, many efforts are being initiated to promote the use of PHEVs as an innovative and affordable transportation system. In order to achieve making the consumers aware of the adoption of PHEVs, we used a model which is based on the extended theory of planned behavior (TPB). This review is based on the factors affecting the adoption of PHEVs among Malaysian consumers. The model takes into account the ten key features that influence the adoption of PHEVs, such as environmental concern, personal norm, attitude, vehicle ownership costs, driving range, charging time, intention, subjective norm, perceived behavioral control, and personal norm. All these constructs are drivers towards the adoption of PHEVs. These factors affect the relationship between the adoption of PHEVs and how consumers intend to protect the environment. This review is based on improving how the “attitude-action” gap is understood as it is an important element for further studies on PHEVs. The aim of the research is to come up with a framework that examines how to modify the consumer’s environmental concerns in acquiring PHEVs. This will pave the way for more academic research and future works that can emphasize how to obtain empirical results. The authors’ recommendation is that, before a consumer’s behavior is assessed and considered, an observation of the current technology is needed with methods and knowledge of the existing technology adoption aspect.

Intelligent energy allocation strategy for PHEV charging station using gravitational search algorithm

Recent researches towards the use of green technologies to reduce pollution and increase penetration of renewable energy sources in the transportation sector are gaining popularity. The development of the smart grid environment focusing on PHEVs may also heal some of the prevailing grid problems by enabling the implementation of Vehicle-to-Grid (V2G) concept. Intelligent energy management is an important issue which has already drawn much attention to researchers. Most of these works require formulation of mathematical models which extensively use computational intelligence-based optimization techniques to solve many technical problems. Higher penetration of PHEVs require adequate charging infrastructure as well as smart charging strategies. We used Gravitational Search Algorithm (GSA) to intelligently allocate energy to the PHEVs considering constraints such as energy price, remaining battery capacity, and remaining charging time.

Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles

Recent researches towards the use of green technologies to reduce pollution and higher penetration of renewable energy sources in the transportation sector have been gaining popularity. In this wake, extensive participation of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. Daytime charging stations will be needed for daily usage of PHEVs due to the limited all-electric range. Intelligent energy management is an important issue which has already drawn much attention of researchers. Most of these works require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. In this paper, gravitational search algorithm (GSA) has been applied and compared with another member of swarm family, particle swarm optimization (PSO), considering constraints such as energy price, remaining battery capacity, and remaining charging time. Simulation results obtained for maximizing the highly nonlinear objective function evaluate the performance of both techniques in terms of best fitness.

Optimisation of PHEV/EV charging infrastructures: a review

Plug-in hybrid electric vehicle (PHEV) or electric vehicle (EV) has the potential to facilitate the energy and environmental aspects of personal transportation, but face a hurdle of access to charging system. The charging infrastructure has its own complexities when it is compared with petrol stations because of the involvement of the different charging alternatives. As a result, the topic related to optimisation of PHEV/EV charging infrastructure has attracted the attention of researchers from different communities in the past few years. Recently introduced smart grid technology has brought new challenges and opportunities for the development of electric vehicle infrastructure facilities. This paper is a review of different computational approaches and techniques used for the optimisation of charging infrastructure for electric vehicles.

Optimized Min-Sum Decoding Algorithm for Low Density Parity Check Codes

Low Density Parity Check (LDPC) code approaches Shannon-limit performance for binary field and long code lengths. However, performance of binary LDPC code is degraded when the code word length is small. An optimized min- sum algorithm for LDPC code is proposed in this paper. In this algorithm unlike other decoding methods, an optimization factor has been introduced in both check node and bit node of the Min- sum algorithm. The optimization factor is obtained before decoding program, and the same factor is multiplied twice in one cycle. So the increased complexity is fairly low. Simulation results show that the proposed Optimized Min-Sum decoding algorithm performs very close to the Sum-Product decoding while preserving the main features of the Min-Sum decoding, that is low complexity and independence with respect to noise variance estimation errors.

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

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.

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

Abstract 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 researches 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 include 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.

Hybrid Swarm Intelligence-Based Optimization for Charging Plug-in Hybrid Electric Vehicle

Plug-in hybrid electric vehicle (PHEV) has the potential to facilitate the energy and environmental aspects of personal transportation, but face a hurdle of access to charging system. The charging infrastructure has its own complexities when it is compared with petrol stations because of the involvement of the different charging alternatives. As a result, the topic related to optimization of Plug-in hybrid electric vehicle charging infrastructure has attracted the attention of researchers from different communities in the past few years. Recently introduced smart grid technology has brought new challenges and opportunities for the development of electric vehicle charging facilities. This paper presents Hybrid particle swarm optimization Gravitational Search Algorithm (PSOGSA)-based approach for state-of-charge (SoC) maximization of plug-in hybrid electric vehicles hence optimize the overall smart charging.

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.

Hybrid bio-Inspired computational intelligence techniques for solving power system optimization problems: A comprehensive survey

Abstract Optimization problems of modern day power system are very challenging to resolve because of its design complexity, wide geographical dispersion and influence from many unpredictable factors. For that reason, it is essential to apply most effective optimization techniques by taking full benefits of simplified formulation and execution of a particular problem. This study presents a summary of significant hybrid bio-inspired computational intelligence (CI) techniques utilized for power system optimization. Authors have reviewed an extensive range of hybrid CI techniques and examined the motivations behind their improvements. Various applications of hybrid bio-inspired CI algorithms have been highlighted in this paper. In addition, few drawbacks regarding the hybrid CI algorithms are explained. Current trends in CI techniques from the past researches have also been discussed in the domain of power system optimization. Lastly, some future research directions are suggested for further advancement of hybrid techniques.