DSP-Based Probabilistic Fuzzy Neural Network Control for Li-Ion Battery Charger

Lin, Faa-Jeng; Huang, Ming‐Shi; Yeh, Po-Yi; Tsai, Han‐Chang; Kuan, Chi-Hsuan

doi:10.1109/tpel.2012.2187073

Cited by 91 publications

(29 citation statements)

References 30 publications

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“…Control methods commonly used in battery charging are: constant current (CC), constant voltage (CV), two-step charging (i.e., CC-CV), pulse charging (PC), reflex charging or negative pulse charging (NPC), trickle charge or taper-current (TC), and float charge (FC) [28][29][30][31].…”

Section: Traditional Charging Control Methodsmentioning

confidence: 99%

A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems

et al. 2018

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Abstract:Energy storage has become a fundamental component in renewable energy systems, especially those including batteries. However, in charging and discharging processes, some of the parameters are not controlled by the battery's user. That uncontrolled working leads to aging of the batteries and a reduction of their life cycle. Therefore, it causes an early replacement. Development of control methods seeks battery protection and a longer life expectancy, thus the constant-current-constant-voltage method is mostly used. However, several studies show that charging time can be reduced by using fuzzy logic control or model predictive control. Another benefit is temperature control. This paper reviews the existing control methods used to control charging and discharging processes, focusing on their impacts on battery life. Classical and modern methods are studied together in order to find the best approach to real systems.

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Section: Traditional Charging Control Methodsmentioning

confidence: 99%

A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems

et al. 2018

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“…To verify the effectiveness of the proposed power smoothing control strategy, an actual 12-hour PV power data from the PV power plant built at the University of Queensland, Australia, [28] is adopted. In Figures 4(a), 4(b), 4(c), 4(d), and 4(e), the PV power data is selected from 6 o'clock to 18 o'clock, and the comparison of smoothing methods include the average method, the moving average method, the low-pass filter method, the PFNN method proposed in [20], and the proposed PFNN-AMF method, where P pv is the actual PV power and P out in the smoothed power. Moreover, P out can be calculated from i * qg The required energy capacity and the required power capacity of the BESS could be described by the following equation:…”

Section: Comparison Of Smoothing Methodsmentioning

confidence: 99%

“…The PNNs can handle the uncertainties effectively and have been widely used in industrial applications [17][18][19]. According to the aforementioned advantages of FNNs and PNNs, the probabilistic FNN (PFNN), which combines the characteristics of FNN (degree of truth) and the characteristics of PNN (probability of truth), has been applied in some areas such as modeling and control problems [20,21]. In addition, if the dimensions of the standard Gaussian are directly extended in asymmetric membership functions (AMFs), not only the learning capability of the networks can be upgraded but also the number of fuzzy rules can be optimized [22,23].…”

Section: Introductionmentioning

confidence: 99%

Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function

Lin

Chao

et al. 2017

International Journal of Photoenergy

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An intelligent PV power smoothing control using probabilistic fuzzy neural network with asymmetric membership function (PFNN-AMF) is proposed in this study. First, a photovoltaic (PV) power plant with a battery energy storage system (BESS) is introduced. The BESS consisted of a bidirectional DC/AC 3-phase inverter and LiFePO4 batteries. Then, the difference of the actual PV power and smoothed power is supplied by the BESS. Moreover, the network structure of the PFNN-AMF and its online learning algorithms are described in detail. Furthermore, the three-phase output currents of the PV power plant are converted to the dq-axis current components. The resulted q-axis current is the input of the PFNN-AMF power smoothing control, and the output is a smoothing PV power curve to achieve the effect of PV power smoothing. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the PV power smoothing control using PFNN-AMF. In addition, a personal computer-(PC-) based PV power plant emulator and BESS are built for the experimentation. From the experimental results of various irradiance variation conditions, the effectiveness of the proposed intelligent PV power smoothing control can be verified.

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“…Due to the popularity of battery charger in many areas of application, it is essential to design a battery charger to provide high performance and high power demands while meeting low cost requirements [1]. Charging battery may seem like an easy and simple task, however, it could involve complicated control algorithm.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Battery Charger Using Ultrasparse Matrix Rectifier with Ann Control

Kumar¹

2014

IJIRSET

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Artificial Neural Networks (ANN) is introduced by McCulloch and Pitts in 1943.It is an interconnected group of nodes. Neural networks models in artificial intelligences are usually referred to as artificial neural network. A novel unity power factor battery charger conception based on an Ultrasparse Matrix Rectifier (USMR) with Artificial Neural Network (ANN) is presented in this paper. Ultrasparse matrix converter topology can be derived from Indirect converter topology having a reduced number of input switches. The circuit has only three switching components and low-energy-conversion loss. This reduces the cost and enhances the system's efficiency for high voltage battery. Thus, the proposed ANN controller improves the line power quality and delivers admissible output power to the battery. The charging method chosen is the Constant Current (CC) or Constant Voltage (CV) method. The ANN control and space vector modulation algorithms are implemented with the Digital Signal Processor.KEYWORDS: ANN controller, battery chargers, fuzzy logic controller (FLC), space vector modulation (SVM), ultrasparse matrix rectifier (USMR), unity power factor. INTRODUCTIONDue to the popularity of battery charger in many areas of application, it is essential to design a battery charger to provide high performance and high power demands while meeting low cost requirements [1]. Charging battery may seem like an easy and simple task, however, it could involve complicated control algorithm. The control algorithm must be able to provide the means to protect the battery from over-charging, which leads to the destruction of the battery. Thus a proper battery charger with the ability of fast charging and efficient control topology is required to ensure the adequacy of the battery system throughout its life [2]. The conventional battery charger experiences a highly distorted current harmonic waveform and low power factor due to the presence of high ripple in the charging current. Thus the battery storage system requires an additional charging circuit for reducing the ripple and extending the battery life. Several methods for charging battery [7] have been proposed depending on the battery chemical composition and the capacity, construction techniques and application of the battery. The efficiency of the charging methodology is measured by the charging current and voltage level, the charging current oscillation, and temperature fluctuation during charging process, also the total charging time. Constant voltage (CV) and constant current (CC) charging are the usual battery charging techniques. The CV charging method keeps the battery voltage constant by using an equivalent series resistance to control the battery current. The CC charging technique maintains the charging current constant, until the battery voltage achieves a designated value. Existing system consists of double control loop based on the fuzzy logic controller to charge the battery [5]. The main drawback of using fuzzy logic controller is that there is no standard and sy...

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DSP-Based Probabilistic Fuzzy Neural Network Control for Li-Ion Battery Charger

Cited by 91 publications

References 30 publications

A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems

A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems

Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function

Design and Implementation of Battery Charger Using Ultrasparse Matrix Rectifier with Ann Control

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