ABSTRACT
This work modified an existing lead-acid battery charging system by developing and simulating the conventional lead-acid battery and the fuzzy logic control of the lead-acid battery charging systems. The fuzzy logic system is developed using Mamdani inference system in Matlab toolbox and Simulink, and consequently employed in controlling the
conventional charging system adopted. The output voltage of the battery is controlled using matrix laboratory (MATLAB) in creating Fuzzy Inference system (FIS). FIS is created by typing “fuzzy” in MATLAB workspace and clicking “Edit” selecting “add variable”, and click input to increase the number of inputs to two. The inputs are labeled voltage error (e) and derivative of error (De/dt). The membership function of the inputs, Mandani, and output blocks and the parameters of theses blocks are filled as shown in Figures 3.7 to 3.9, respectively. Furthermore, the Mandani rules are editted as shown in Figure 3.11. The model shows that the power outputs from the solar cells increased linearly when the voltage changes from 0 to 250V and then increased exponentially from 250 to 420V. The curves showed that the power outputs from the solar cells increased linearly when the voltage changes from 0 to
250V. The increase in voltage from 300 to 350V produces a maximum output power of about
1400W at 20 oC and 1000 W/m2. For the same range of voltage at 400C and 1000 W/m2, the maximum output power is approximately 1200W. Similarly, at 200C and 500 W/m2, and 40
0C and 500 W/m2 the maximum output power is 550W and 500W, respectively. In the case of
200C and 200 W/m2 and 400C and 200 W/m2 the maximum output power is 240W and 235W, respectively. The collector current increased from 0 to 800 ampere with the increase in the collector-emitter voltage from 1 to 5V. The only exception is the case where emitter-voltage (Vge) is 8V.
CHAPTER ONE
INTRODUCTION
1.1 Background to the Study
Lotfi Zadeh, the father of fuzzy logic, claimed that many sets in the world that surround us are defined by a non – distinct boundary. Zadeh decided to extend two-valued logic, defined by the binary pair {0, 1}, to the whole continuous interval [0, 1], thereby introducing a gradual transition from falsehood to truth [1, 2]. It is an extension of convectional Boolean logic constructed to handle the concept of partial truth, that is, statements that are neither completely true nor completely false [3]. Fuzzy control is a control method based on fuzzy logic. Just as fuzzy logic can be described simply as “computing with words rather than numbers”; fuzzy control can be described simply as “control with natural language rather than equations” [4]. Natural language abounds with intrinsic vagueness or ambiguity, incompleteness and imprecise concepts difficult to translate into a final description that is crisp, except for tolerance. This may lead to potential problems in safety-critical situation. Fuzzy set theory provides mathematical tools for carrying out approximate reasoning processes when available information is uncertain, incomplete, imprecise, or vague [5, 6]. A fuzzy controller can include empirical rules, and that is especially useful in operator controlled plants [7]. The fuzzy logic concept incorporates an alternative way which allows one to control a charging system using a higher level of abstraction without knowing the plant model. Unlike the conventional charging system, if the mathematical model of the process is unknown, fuzzy logic can still simulate the process with a certain guaranteed performance [6]. It starts with heuristics and human expert knowledge (in terms of fuzzy IF- THEN rules) which can be extended to the charging system. It provides a formal
methodology for representing, manipulating and implementing this human heuristic knowledge on a battery charging system.
Nowadays portable electronic devices have become the main applications of advanced technical products – mobile phones, laptops, MP3 players, etc. So the battery charging system which forms an important and essential power source for the battery charging and the driving of load AC/DC is common [8]. It is commonly anchored on power electronic conversion from direct current (DC) to alternating current (AC), vice versa. Examples include: (i) the solar-based boost differential single phase inverter using a PV source [as1], (ii) GSM -based transformer monitoring system using an AC source, (iii) a PV-based supply which uses the maximum power point tracking technique; etc [8, 9, 10] (iv) wind generator using an AC source to charge battery bank [11]. These systems have to be efficient, provide a fast charging mode, and guarantee the safety of battery from damage due to discharge and overcharge condition. The basic essential items the designer of battery should have to check include voltage, energy density, temperature performance, drain rate, and cycle life. As for the recharging of batteries, several types of chargers, which deliver a constant voltage charge whereby the charge current is reduced to zero has been proposed [12]. However, this way of charging came with a non-optimal charging profile, hence resulting in long charging interval. Other sophisticated chargers that employ high charging currents during the initial stage of charging cycle, which reduces gradually till the battery is full charged are also investigated. This technique is good, but the small charging current at the final stage slows down the charging rate of the battery. There is, therefore, need to introduce a controlled charging system with the aim of improved charging rate. This need informs the inclusion of the fuzzy control technique into the conventional system in order to achieve a better battery charging performance. The intention of applying this intelligent technique is to reduce the extent of battery damage considerably. The advantage of using fuzzy logic is its ability to produce
improved performance based on expert knowledge. In this thesis, fuzzy logic was applied in the control of battery charging in order to produce an optimum charging condition – increased charger efficiency.
1.2 Statement of Problem
The main factor that contributes to prolonging the life of a battery and increasing its performance can be traced down mainly to the charging system of the battery. Battery overcharging, as well as preventing the discharge of the battery below a particular voltage level is crucial in charging system. The basic essential issues designers address include current, voltage, energy density, temperature performance, drain rate, and life-cycle of the battery and the designer must have minimize size, weight and the capacity of the battery. Battery longevity is one of important issues considered by researchers. Rechargeable battery life is determined by not only charging times, but also overcharging controls built into the charger. Therefore, battery charger with optimum efficiency is proposed. It has been established that the life cycle of Li-Ion batteries is influenced by charger controller, which has the duty of identifying the charging point in order to avoid rechargeable battery damage. Therefore, fuzzy logic approach is efficient for controlling the charging and discharging of battery.
1.3 Objectives of the Research
The objectives of this work are to model and simulate a typical battery charging system and carry out performance analysis of a fuzzy logic controlled battery system and the conventional battery charging systems using MATLAB Simulink environment. The results of
the fuzzy controlled battery system are compared with a classical control approach. The characteristics of the photovoltaic system which is a major focused of this work are analyzed with respect to temperature change and irradiance.
1.4 Scope
In this project, the application of fuzzy logic control is confined to battery charging, where the fuzzy logic system is modeled and simulated to replicate the effect of fuzzy logic control charging effect of a battery (lead-acid battery) as compared with the conventional battery charging system. After that, the results are compared and conclusions are drawn from the results obtained and, the power and voltage of the solar cells, collector current of the inverter are determined.
1.5 Justification
This work is justified by the use of photovoltaic cells (PVs) and other renewable energy for charging powering loads such as the base stations of mobile systems, refrigerators, poultry the electrical grid systems, etc., [10]. The findings of this work would be beneficiary to the industry, electrical power system engineers and to students of electrical and electronic engineering.
1.6 Methodology
This work looked at the physical structures and the models which constitute a lead-acid battery charging systems from already published works. It adopted and made improvement on one of the models by adding a photovoltaic system, and fuzzy control in the conventional charging system.
1.7 Outline of the thesis
This dissertation comprises five chapters. Chapter one gives a brief background statement of the work. It discusses the statement of the problems, objective, scope, justification, methodology, and outline of the thesis. Chapter two presents theories on the photovoltaic cell, solar converter, batteries, etc. It also presented related works on the state-of-the-acts in order to discover area of study to be address by this work. Modelling and simulation is presented in chapter three while chapter four discusses the results obtained from the simulation. Finally, chapter five presents the conclusion of the dissertation.
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