View of DESIGN OF THE PROPOSED FUZZY LOGIC-BASED MODEL WITH DUAL WIND FLOW CONTROLLERS FOR BETTER EV CHARGING PERFORMANCE

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ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING Peer Reviewed and Refereed Journal, ISSN NO. 2456-1037

Available Online: www.ajeee.co.in/index.php/AJEEE

DESIGN OF THE PROPOSED FUZZY LOGIC-BASED MODEL WITH DUAL WIND FLOW CONTROLLERS FOR BETTER EV CHARGING PERFORMANCE

Raja Kokate

EEE Department, IPS Academy, Institute of Engineering and Science, Indore (M.P.), India Deepesh Bhati

EEE Department, IPS Academy, Institute of Engineering and Science, Indore (M.P.), India Manish Sahajwani

EEE Department, IPS Academy, Institute of Engineering and Science, Indore (M.P.), India

Abstract - The need for charging stations is expected to increase in tandem with the expansion of the electric car market. Estimating how quickly electric cars will become prevalent in the market is made difficult by the diverse set of market conditions and the exponential increase in the number of available electric vehicles. For the adoption of electric vehicles for use as light passenger vehicles, both an aggressive and a gradual scenario has been presented. The pricing of electric cars on the market, as well as tax cuts and incentives, have all had a key role in the pace of electric vehicle adoption. We set out to design a stochastic joint planning strategy that takes into consideration the way electric vehicles will operate in both the transportation and power sectors. This technique will take into account how EVs will behave. Renewable energy sources, such as solar and wind power, have gained favor as an alternative to traditional energy sources since they do not have an effect on climate change. This study proposes a charging model for electric cars (EVs) that optimizes charging while taking into account the aging effects of cycle batteries. EVs are vehicles that run on electricity rather than on gasoline or diesel.

Keywords: Electric vehicle, Battery, Fuzzy logic, Renewable energy.

1. INTRODUCTION

Controlling the amount of power used from the battery charging electric cars is one of the aims of smart grids, as it will help boost the efficiency of the operation of the power system. As a direct result of this, the power system is often brought up whenever goals and restrictions are being discussed. However, it is essential to bear in mind that charging an electric vehicle (EV) may have an effect not only on the power supply but also on the longevity of the battery. This is something that must be kept in mind. A multi-objective optimization, as described in references [1] and [2], may help reduce the rate of battery degradation and overall energy expenses associated with plug-in hybrid electric vehicles. Even when beginning charging in a more advantageous position and making use of less power, charging rates are not improved. Because of this, each vehicle has a Pareto front consisting of numerous optimal alternatives. In order to arrive at a conclusion, it is necessary to take into account the amount of money spent on energy and the amount of degradation experienced by the battery. The creation of the solid electrolyte interphase (SEI) layer is one of the effects that comes with the aging of a battery. Even though it is the method that is most relevant to the battery that was used, it may not be the best strategy to explain the aging of the battery when considered in the context of a charging optimization model. According to the research found in [3] and [4], the average state of charge (SOC) may have a considerable impact on the lifespan of a battery. The cost of charging electricity and the cost of battery deterioration are both affected by the many charging techniques, including cost-optimized charging and charging to maximize battery longevity. These charging methods are among the many that are being investigated. The SOC has an effect on the calendar and cycle battery aging of the model, but different charging rates do not have any impact on it.

In order to recharge their batteries, electric vehicles (EVs) often need to be hooked into an external power supply. The number of electric vehicles now on the road as well as the number of different types of EVs currently on the market are pushing up the need for charging stations. Even though there aren't a lot of public charging stations right now, both the public sector and the commercial sector are pushing for more to be built. The adoption of public charging stations is being driven by governmental legislation as well as costs on the market. The worldwide market for electric vehicle (EV) chargers is projected to rise from

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more than one million units in 2014 to more than 12.7 million units in 2020, as stated in a recent report titled "EV Charging Infrastructure Report" produced by HIS Inc.

There are two different kinds of charging for electric vehicles: charging at the destination and charging while traveling. Locations that are suitable for charging might include, for instance, your own house, your place of employment, or aparking lot. A destination's charging requirements may often be satisfied by using either public or private charging outlets.

On-route charging for electric cars is mostly accomplished via the use of battery swap stations and fast charging service stations (BSS). Since the majority of people's typical commutes fall below the range of reachable electric vehicles, the method of charging electric vehicles that is advised is destination charging. In the event that it is essential to drive over long distances, the quick charging station and the battery changeover station will serve as an important backup source of power. Fast charging at Level 1, Level 2, and DC levels Charging stations for electric vehicles are often categorized into one of three categories according to the level of service and amount of charging power that they provide. Chargers for electric cars are available in a dizzying array of forms and sizes, each of which may correspond to a particular service mode, consumer demographic, or set of technical needs.

2. REVIEW ON DIFFERENT MODELS FOR IMPROVING EV CHARGING PERFORMANCE 3. More recently, intense marketing and strong governmental support have pushed the development of both battery and electric power train technology. This has helped to speed up the process. The price that must be paid to manufacture batteries has significantly decreased over the course of the last three years. Electric vehicles are likely going to play a more dominant position in the automobile marketplaces of the future. As a result of the EV transition scenario and the continuous use of the roll-out schedule established in 2015 [5], according to the statistics, there were more over one million EVs on the road in October of 2018. [5] These statistics were derived from the usage of the roll-out timetable. In point of fact, the federal government of the United States has proposed a variety of initiatives to encourage the development of electric vehicle (EV) charging infrastructures in public spaces. In addition, information obtained from the Canadian Ministry of Transportation indicates that the province of Ontario invested $20 million in 2017 to build 500 electric vehicle charging stations at around 250 locations [6]. Additionally, the German National Platform for Electric Mobility forecasted that there would be around one million EVs on the road by the year 2020 and that there would be a need for more than 70.000 charging stations, the majority of which would be located on roadways (CPs). China came up with a scheme to designate a set number of charging stations utilizing solar-based charging systems in order to get over the limitations of RES consumption and satisfy the growing need for electric vehicles' energy [7]. The United States of America, China, Germany, Sweden, the Netherlands, Norway, Japan, France, the United Kingdom, and Canada were among the ten countries that participated in intergovernmental discussions on EV- related projects in May of 2017. These discussions were held with the goal of accelerating the global adoption of electric vehicles. Automakers in a variety of countries have lately begun offering new electric car (EV) models in an attempt to satisfy the demand from customers.

These new EV models include the battery electric vehicle (BEV), which is more reasonably priced, and the plug-in hybrid electric vehicle (PHEV). In addition, in order to advance and broaden the market for electric vehicle charging infrastructure, utility and power companies have been working together with other stakeholders [8]. In spite of the significance of the aforementioned laws and regulations, these countries do not have electric vehicle charging infrastructure that is dependable and adequate. Electric vehicles, acting as a random load, will alter the overall load characteristics of the distribution network. This will make load forecasting far more difficult, and it will also have an effect on grid architecture and substation planning.

The impact of electric vehicle demand should be carefully considered at every stage of the planning and design procedures for the distribution grid [9], as this will ensure that the planned grid will be able to satisfactorily meet all of the numerous demands associated with EVs.

Although a number of research projects have been carried out, there are not a lot of publications that provide a comprehensive evaluation of the concerns about the difficulties and problems associated with the integrated PV-EV charging's viability [10, 11]. In addition, there aren't any definite laws in place that address the problems with the

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ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING Peer Reviewed and Refereed Journal, ISSN NO. 2456-1037

charging infrastructure and the EV charging timetable at this time. This chapter offers an overview of the variouscharging station layouts and control topologies that may be used in order to facilitate efficient management of electric vehicle charging stations.

4. DESIGN OF THE PROPOSED FUZZY LOGIC-BASED MODEL WITH DUAL WIND FLOW CONTROLLERS FOR BETTER EV CHARGING PERFORMANCE

This article proposes the implementation of a hybrid energy system that makes use of photovoltaics, wind turbines, fuel cells, as well as a battery storage system. As a result of this, it has been concluded that the generation of electricity throughout the whole year may be achievable by combining solar panels, wind turbines, and fuel cells. The architecture of the DC bus is currently under discussion for the configuration. In order to avoid power outages caused by the unpredictability of solar irradiation and wind speed, the purpose of this model is to ensure that the state of charge (SOC) of the battery bank is kept as constant as is humanly feasible. Fig. 1 provides an illustration of the hybrid power system that was investigated during this study.

Figure 1 Schematic of the proposed model

The first stage in attaining the product's trademark sharpness is to eliminate the product's fuzzy ends. Defuzzification is the process of taking the result of a fuzzy set and transforming it into a single, accurate value. This controller's de-fuzzed values show which actions must be taken to control a method in order for it to work properly. The process cannot be controlled without these procedures.

Fig 2 depict TR, and exhibits MFs of the output current command, respectively. This is all in

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Therefore, in the proposed FLC, triangular MFs are utilized to represent both the input variables and the output variables in order to reduce the amount of computational complexity. Take note that the charging method for this study consisted of all five stages. As a direct consequence of this, there are a total of five output MFs. There are five different language values that may be mapped to the TR and TR input variables if they were to be translated. As a direct consequence of this, the planned FLC will have a rule base consisting of twenty-five different rules. As the fundamental foundation for design, the charging current is dynamically changed in reaction to variations in temperature. It is possible to improve charge rates for a range of input sources by using a controller that makes use of fuzzy logic

Figure 2 Use of FLC for controlling battery charging status

The act of taking the output of a fuzzy set and converting it into a single, correct value is referred to as defuzzification. In a symbolic logic controller, defuzzed values indicate the steps that need to be done to control the method in order for this controller to be functional. These steps are necessary for controlling the method. The use of symbolic logic gives a computer the capacity to simulate human thinking, which may be explained as follows: The modern world is defined by having ideals that aren't really clear and having goals that aren't met. Following the consideration of all of the pertinent facts, fuzzy sets and rules are used to arrive at a conclusion about the level of expertise possessed by a certain individual. The use of symbolic logic, which is a kind of computing that is based on words, is one method that may be used to arrive at these discoveries.

5. RESULT EVALUATION & COMPARISON

An examination of a standalone system that had three different types of renewable energy sources was carried out with the use of MATLAB SIMULINK. These sources were photovoltaic cells (PV), PMSG wind turbines, and fuel cells. In addition to it, the system had a battery storage device. When combined, these three distinct categories of renewable energy sources have the ability to provide forty kilowatts of power altogether. The charge management controller is in charge of managing the amount of voltage and current that is supplied to the battery while it is being charged. This is done so that the battery may be charged to the appropriate level. The utility of it was examined via the use of a range of case studies that concentrated on the immediate future. In order to accurately recreate the circumstances of a transient, an additional load of ten kilowatts was required. After being activated for one brief second, this load was deactivated for the remainder of the remaining time. The purpose of doing this was to make it easier to compare the outcomes of the various experiments. There was an issue with one of the three phases at the 0.4 second mark, which was later resolved at the 0.7 second mark. The battery that is being charged and the DC capacitor that may be located on the inverter both have the same voltage (Vdc).

When we examine the two side-by-side, we are able to better appreciate the progress that has been made. PSO, APSO, AAPSO, and FUZZY are some of the optimization algorithms that were employed in this process in order to get the best possible outcomes.

The results of the simulations indicate that using FUZZY as a solution is going to be the most effective course of action in any one of these situations (conventional PID

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controller and FOPID type). When presented with challenging conditions, the most fruitful outcomes are often achieved with the least amount of time spent restoring calm as a response. This is because the most fruitful outcomes typically require less time spent regaining composure. The outcomes of the simulation are shown in great depth throughout Tables 1 through 4, which may be seen below. According to the collected evidence, battery charge regulating controllers are superior to more traditional methods of charging batteries in terms of their ability to execute their designated tasks.

Table 1 Performance of VDC in case of Conventional PID Controller PSO APSO AAPSO FUZZY

Maximum Overshoot 670 645 642 640 Settling Time 0.2 0.18 0.16 0.143 Table 2 Performance of VDC in case of FOPID Controller

PSO APSO AAPSO FUZZY Maximum Overshoot 680 677 660 656 Settling Time 0.2 0.07 0.05 0.04

Table 3 Performance of battery charging current in case of PID Controller PSO APSO AAPSO FUZZY

Maximum Overshoot 48 45 41 38 Settling Time 0.05 0.035 0.03 0.028

Table 4 Performance of battery charging current in case of FOPID Controller PSO APSO AAPSO FUZZY

Maximum Overshoot 50 48 26 20 Settling Time 0.055 0.04 0.036 0.024

During the course of this experiment, a hybrid system including solar panels, wind turbines, fuel cells, and storage batteries was used in order to successfully provide the necessary load. Using an intelligent battery charger controller, the goal of this study is to increase the amount of time that storage batteries can be put to productive use. This will be accomplished by extending the amount of time that storage batteries can be used. One of the two control PID loops that are included within the battery charger is responsible for regulating the charging voltage of the boost converter, and the other is responsible for regulating the charging current that is supplied to the batteries. Both of these loops are located inside the battery charger. These controllers, which were based on AI, were efficient in the sense that they were able to do what they set out to do. The functioning of this component makes use of a FOPID controller rather than the more common PID controller.

Using one of many different AI-based optimization algorithms is one way to go about selecting the best possible values for the controller's settings when it comes to charging the battery. The fact that software programs like MATLAB and Simulink were used to build the model is evidence that the processes that were provided are effective. The simulations show that in terms of overall performance, the FUZZY algorithm is better to both the FOPID type and the standard PID battery charging controller.

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