Hybrid Particle Swarm and Gravitational Search Optimization Techniques for Charging Plug-In Hybrid Electric Vehicles

Hybrid Particle Swarm and Gravitational Search Optimization Techniques for Charging Plug-In Hybrid Electric Vehicles

Imran Rahman (Universiti Teknologi Petronas, Malaysia), Pandian Vasant (Universiti Teknologi Petronas, Malaysia), Balbir Singh Mahinder Singh (Universiti Teknologi Petronas, Malaysia) and M. Abdullah-Al-Wadud (King Saud University, Saudi Arabia)
DOI: 10.4018/978-1-4666-9644-0.ch018
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Abstract

Electrification of Transportation has undergone major modifications since the last decade. Success of combining smart grid technology and renewable energy exclusively depends upon the large-scale participation of Plug-in Hybrid Electric Vehicles (PHEVs) towards reach the desired pollution-free transportation industry. One of the key Performance pointers of hybrid electric vehicle is the State-of-Charge (SoC) which needs to be enhanced for the advancement of charging station using computational intelligence methods. In this Chapter, authors applied Hybrid Particle swarm and gravitational search Optimization (PSOGSA) technique for intelligently allocating energy to the PHEVs considering constraints such as energy price, remaining battery capacity, and remaining charging time. Computational experiment results attained for maximizing the highly non-linear fitness function estimates the performance measure of both the techniques in terms of best fitness value and computation time.
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Introduction

Recent researches on green technologies for transportation sector are gaining popularity among the research communities from different areas. In this wake, Plug-in hybrid electric vehicles (PHEVs) have great future because of their charge storage system and charging facilities from traditional grid system. Several researchers have proved that a great amount of reductions in greenhouse gas emissions and the increasing dependence on oil could be accomplished by electrification of transport sector (Caramanis & Foster 2009). Future transportation sector will depend much on the advancement of this emerging field of vehicle optimization. Indeed, the adoption of hybrid electric vehicles (HEVs) has brought significant market success over the past decade. Vehicles can be classified into three groups: internal combustion engine vehicles (ICEV), hybrid electric vehicles (HEV) and all- electric vehicles (AEV) (Tie & Tan, 2013). Plug-in hybrid electric vehicles (PHEVs) which is very recently introduced promise to boost up the overall fuel efficiency by holding a higher capacity battery system, which can be directly charged from conventional power grid system, that helps the vehicles to operate continuously in “all-electric-range” (AER). All-electric vehicles or AEVs is a kind of transport which use electric power as only sources to run the system. Plug-in hybrid electric vehicles with a connection to the smart grid can own all of these strategies. Hence, the widely extended adoption of PHEVs might play a significant role in the alternative energy integration into traditional grid systems (Lund & Kempton, 2008). There is a need of efficient mechanisms and algorithms for smart grid technologies in order to solve highly diverse problems like energy management, cost reduction, efficient charging station etc. with different objectives and system constraints (Hota, Juvvanapudi, & Bajpai, 2014).

According to Electric Power Research Institute (EPRI), about 62% of the whole United States (US) fleet will comprise of PHEVs within the year 2050 (Soares et al., 2013). Moreover, there is an increasing demand to implement this technology on the electric grid system. Large numbers of PHEVs have the capability to make threats to the stability of the power system. For example, in order to avoid disturbance when several thousand PHEVs are introduced into the system over a small period of time, the load on the power grid will need to be managed very carefully. One of the main targets is to facilitate the proper communication between the power grid and the PHEV. For the maximization of customer contentment and minimization of burdens on the grid, a complicated control appliance will need to be addressed in order to govern multiple battery loads from a numbers of PHEVs properly (Su & Chow, 2012a). The total demand pattern will also have an important impact on the electricity production due to differences in the needs of the PHEVs parked in the deck at certain time (Su & Chow, 2011). Proper management can ensure strain minimization of the grid and enhance the transmission and generation of electric power supply. The control of PHEV charging depending on the locations can be classified into two groups; household charging and public charging. The proposed optimization focuses on the public charging station for plug-in vehicles because most of PHEV charging is expected to take place in public charging location (Su & Chow, 2012). Wide penetration of PHEVs in the market depends on a well-organized charging infrastructure. The power demand from this new load will put extra stress on the traditional power grid (Morrow, Karner, & Francfort, 2008). As a result, a good number of PHEV charging stations with suitable facilities are essential to be built for recharging electric fleet, for this some strategies have been proposed by the researchers (Mayfield, Jul. 2012). Charging stations are needed to be built at workplaces, markets/shopping malls and home. Boyle (2007) proposed the necessity of building new smart charging station with effective communication among utilities along with sub-station control infrastructure in view of grid stability and proper energy utilization. Furthermore, sizeable energy storage, cost minimization; Quality of Services (QoS) and intelligent charging station for optimal power are underway (Hess et al., 2012). In this wake, numerous techniques and methods were proposed for deployment of PHEV charging stations (Z. Li, Sahinoglu, Tao, & Teo, 2010).

Key Terms in this Chapter

Vehicle-to-Grid: In vehicle-to-grid (V2G) concept, an electric vehicle acts both as a load and power source in smart grid environment. A V2G-capable vehicle offers reactive power support, active power regulation, tracking of variable renewable energy sources, load balancing, and current harmonic filtering. These technologies can enable ancillary services, such as voltage and frequency control and spinning reserve. Success of the V2G concept depends on standardization of requirements and infrastructure decisions, battery technology, and efficient and smart scheduling of limited fast-charge infrastructure.

State-of-Charge: State-of-Charge (SoC) of a PHEV battery is expressed as the ratio of its capacity of current to the nominal capacity . The nominal capacity is known by the vehicle manufacturer and shows the maximum amount of charge that can be stored in the battery. SoC estimation is a fundamental challenge for battery use. The SoC of a battery, which is used to describe its remaining capacity, is a very important parameter for a control strategy. The SoC can be defined as follows:

Fitness Function: A necessary characteristic of evolutionary structural testing is that the fitness function is constructed on the basis of the software under test. The fitness function itself is not of interest for the problem; however, a well-constructed fitness function may substantially increase the chance of finding a solution and reaching higher coverage. Better guidance of the search can result in optimizations with less iterations, therefore leading to savings in resource expenditure.

All Electric Range: All electric range is a mode of electric vehicle when it is only run by charged batteries in order to reduce the overall fuel consumption. Calculation of all electric range varies according to the designs of the hybrid electric vehicles. The “all electric range” (AER) test quantifies the electric-only miles possible with the battery for a particular configuration and vehicle class.

Energy Security: The interest in energy security is based on the notion that an uninterrupted supply of energy is critical for the functioning of an economy. However, an exact definition of energy security is hard to give as it has different meanings to different people at different moments in time. It has traditionally been associated with the securing of access to oil supplies and with impending fossil fuel depletion. With an increase in natural gas use, security concerns also arose for natural gas, widening the concept to cover other fuels. Because oil is nowadays a globally traded commodity, physical shortages show up in the price of oil on the world market, in the form of a long-term increase and of short-term fluctuations.

Plug-In Hybrid Electric Vehicles: Plug-in Hybrid Electric Vehicles (PHEVs) are being made with relatively large sized batteries that can be charged during off-peak hours, and permit the vehicle owner to use exclusively electric made for 30 – 60 miles of driving as well as switching into traditional gasoline for longer trips. PHEVs offer customers the opportunity for fuel at gasoline-equivalent prices of less than $1.00 per gallon. For a given size battery bank, the range of a PHEV can be prolonged significantly before batteries need recharging by turning on the engine or fuel cell whenever the vehicle power demand exceeds some threshold.

Smart Grid: Smart grid is an intelligent bi-directional electrical power system. It ensures most advanced and efficient communication network between suppliers and consumers of electricity. Unlike traditional power grid, smart grid offers better system sustainability and network security. The “smart grid” includes advanced utility Supervisory Control and Data Acquisition (SCADA) systems that can keep track of thousands of data points of loads and resources, smart meters that can communicate to the utility SCADA center, and smart appliances that can respond instantaneously to economic or reliability imperatives

Smart Charging: Smart charging refers to the intelligent control of electric vehicle charging by the assigned authority. Smart charging can be both direct and indirect depending upon the user demand and available infrastructure. The main concept of smart charging lies in the charging of vehicle when the price and demand are lowest as well as excess amount of available capacity.

Particle Swarm Optimization: Particle Swarm Optimization (PSO) algorithm was introduced by Kennedy and Eberhart in 1995, which is a heuristic global optimization method and a member of swarm intelligence family. PSO is a computational intelligence-based technique that is not largely affected by the size and nonlinearity of the problem, and can converge to the optimal solution in many problems where most analytical methods fail to converge.

Charging Station: Charging station is an important component for the healthy growth of the electric vehicle industry. Charging station refers to an infrastructure similar to petrol station (for conventional vehicle) that provides electric energy for the charging of plug-in hybrid electric vehicles (PHEVs). Many charging stations are on-street facilities provided by electric utility companies, mobile charging stations have been recently introduced. From the grid standpoint, a charging station is one way that the operator of an electrical power grid can adapt energy production to energy consumption, both of which can vary randomly over time. Basically, EVs in a charging station are charged during times when production exceeds consumption and are discharged at times when consumption exceeds production. In this way, electricity production need is not drastically scaled up and down to meet momentary consumption, which would increase efficiency and lower the cost of energy production and facilitate the use of intermittent energy sources, such as photovoltaic and wind.

Gravitational Search Algorithm: Gravitational Search Algorithm (GSA) is a heuristic optimization algorithm which has been gaining much interest among the scientific community recently. GSA is a nature inspired algorithm based on the Newton’s famous law of gravity and the law of motion. GSA is classified under population-based method and is reported to be more instinctive. In GSA, the agent has four parameters which are position, inertial mass, active gravitational mass, and passive gravitational mass. GSA is a memory-less algorithm. However, it works efficiently like the algorithms with memory.

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