Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

Optimized Fuzzy Logic Control Strategy for Parallel Hybrid Electric Vehicle Based on Genetic Algorithm

2013 ◽  
Vol 274 ◽  
pp. 345-349 ◽  
Author(s):  
Mei Lan Zhou ◽  
Deng Ke Lu ◽  
Wei Min Li ◽  
Hui Feng Xu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Fuzzy Logic Control ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Membership Functions ◽  
Logic Control

For PHEV energy management, in this paper the author proposed an EMS is that based on the optimization of fuzzy logic control strategy. Because the membership functions of FLC and fuzzy rule base were obtained by the experience of experts or by designers through the experiment analysis, they could not make the FLC get the optimization results. Therefore, the author used genetic algorithm to optimize the membership functions of the FLC to further improve the vehicle performance. Finally, simulated and analyzed by using the electric vehicle software ADVISOR, the results indicated that the proposed strategy could easily control the engine and motor, ensured the balance between battery charge and discharge and as compared with electric assist control strategy, fuel consumption and exhaust emissions have also been reduced to less than 43.84%.

scholarly journals Design and Implementation Buck Converter for 540WP Solar Charger Using Fuzzy Logic Control

2018 ◽  
Vol 43 ◽  
pp. 01009
Author(s):  
Sutedjo ◽  
Ony Asrarul Qudsi ◽  
Andi Ardianto ◽  
Diah Septi Yanaratri ◽  
Suhariningsih ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Duty Cycle ◽  
Fuzzy Logic Control ◽  
Buck Converter ◽  
Test Results ◽  
Constant Voltage ◽  
Battery Charging ◽  
Set Point ◽  
Design And Implementation ◽  
Logic Control

This paper presents the details of design and implementation of DC-DC Buck converter as solar charger. This converter is designed for charging a battery with a capacity of 100 Ah (Ampere Hours) which has a charging voltage of 27.4 volts. The constant voltage method is selected on battery charging with the specified set point. To ensure the charging voltage is always on the set point, the duty cycle control of buck converter is set using Fuzzy Logic Control (FLC). The design implementation has been tested on PV (photovoltaic) with 540WP capacity. Based on the test results, this method is quite well implemented on the problem charger

scholarly journals MODELING AND SIMULATION OF ROTOR FLUX OBSERVER BASED INDIRECT VECTOR CONTROL OF INDUCTION MOTOR DRIVE USING FUZZY LOGIC CONTROL

2014 ◽  
pp. 241-246
Author(s):  
B. MOULI CHANDRA ◽  
S.TARA KALYANI
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Rule Base ◽  
Flux Observer ◽  
Logic Control ◽  
Rotor Flux ◽  
Proportional Integral Controller ◽  
Inductor Motor

The indirect vector controlled inductor motor (IM) drive involves decoupling of the stator current into torque and flux producing components. This paper proposes the implementation of fuzzy logic control scheme applied to a two d-q current components model of an induction motor. A Fuzzy logic Controller is developed with the help of knowledge rule base for efficient and robust control. The performance of Fuzzy Logic Controller is compared with that of the PI controller with rotor flux observer in terms of the settling time and dynamic response to sudden load changes. The harmonic pattern of the output current is evaluated for both fixed gain proportional integral controller and the Fuzzy Logic based controller. The performance of the IM drive has been analyzed under steady state and transient conditions. Simulation results of both the controllers are presented for comparison.

Implementation of a Fuzzy Logic Control Strategy on a Harvester’s Controller Based on MATLAB Environment

2019 ◽  
Vol 33 (13) ◽  
pp. 1959043
Author(s):  
Wei Li ◽  
Jingqian Wen ◽  
Qing Jiang ◽  
Liangtu Song ◽  
Zhengyong Zhang
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Nonlinear Process ◽  
C Language ◽  
Logic Control ◽  
Cleaning System ◽  
Complete Set ◽  
Classical Control ◽  
Upper Computer

Due to the nonlinear process of grain harvesting, there is no precise mathematical model to describe the behavior of the cleaning system of a harvester. Both the classical control and modern control methods cannot fulfil the requirements. Owing to this, the intelligent control algorithm was proposed, and the fuzzy logic control (FLC) method is a type of this. At present, most FLC algorithms are proposed in a MATLAB environment. However, the control problems in reality are controlled by microcomputer controllers with different chips. The control language of the microcomputer controller is usually written in C language. It is impossible to directly migrate the algorithm between these two different languages. Therefore, it is an important issue to transplant the FLC algorithm procedure written by MATLAB to the microcomputer controller. To realize the above target, we have built a complete set of control systems for our harvester’s cleaning system based on an upper computer and an STM32 core-chip controller. By means of combining FLC theory and expert knowledge, we adopted an improved FLC algorithm for the cleaning system, which is mounted in our self-designed combine harvester. Through this scheme, we have realized the objective of migrating the FLC algorithm from a MATLAB environment to the controller. The results of the experiment show that our method is reliable.

scholarly journals A study on modeling of wireless charging system based on fuzzy logic control

2021 ◽  
Vol 1983 (1) ◽  
pp. 012053
Author(s):  
Pengcheng Cao ◽  
Yong Lu ◽  
Changbo Lu ◽  
Xudong Wang ◽  
Wanli Xu ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Wireless Charging ◽  
Charging System ◽  
Logic Control

Design and Tuning of Distributed Importance-Based Fuzzy Logic Controller for Two-Link Rigid-Flexible Manipulator

2003 ◽  
Author(s):  
Linda Z. Shi ◽  
Mohamed B. Trabia
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Vertical Plane ◽  
Fuzzy Rules ◽  
Flexible Manipulator ◽  
Linear Controller ◽  
Logic Control ◽  
Single Link

Fuzzy logic control has been widely used in many industrial processes due to its computationally efficient and robust characteristics. In many applications, verbalization of expert-knowledge can be easily used to design a fuzzy logic controller (FLC). On the other hand, other applications with many variables and complex mathematical model offer challenges to fuzzy logic control. Multi-link flexible manipulators belong to this category. An earlier work, [1], presented a distributed importance-based FLC for a single-link flexible manipulator. This paper extends this idea to a two-link rigid-flexible manipulator that moves in a vertical plane where the gravity field is active. The structure of the proposed controller is based on evaluating the importance degrees of the variables of the system, over its range of operation, to consider the coupling effects between the rigid and the flexible links. Variables with higher importance degrees are grouped together while variables with lesser importance degrees may be deleted to simplify the design of the controller. After determining the importance degrees of the variables, a distributed controller composed of four two-input one-output FLC’s is created. Unlike the single-link flexible manipulator, the fuzzy rules of the distributed FLC for the two-link rigid-flexible manipulator cannot be written by an expert based on intuition and observation of the inertial system due to the complexity of the manipulator and the coupling effect of its variables. To solve this problem, an importance-based linear controller that has the same input-output structure as that of distributed importance-based FLC is constructed to help write the fuzzy rules of the distributed FLC. Fuzzy rules of the distributed FLC are then selected to mimic the performance of the corresponding linear controllers. To compare the performance of the distributed importance-based FLC with that of importance-based linear controller, these two controllers are tuned using nonlinear programming by varying the gains of the importance-based linear controller and the parameters of membership functions of the variables in the distributed importance-based FLC. Robustness of each of the controllers after tuning is tested by varying the payload of the manipulator. The two importance-based controllers are simulated and compared.

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