Prediction study on the degeneration of lithium-ion battery based on fuzzy inference system

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740083 ◽  
Author(s):  
Jian Ping Shi
Keyword(s):  
Lithium Ion Battery ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Lithium Ion ◽  
External Factors ◽  
Inference System ◽  
Predicted Model ◽  
Simulation Results ◽  
Degradation Degree ◽  
Internal And External Factors

The degradation degree prediction of lithium-ion battery has been studied through experimental data. Characterization parameters on the degradation degree of lithium-ion battery were deduced under consideration of the internal and external factors. The analysis of discrete degree was proposed to depict the degradation degree for lithium-ion battery. Furthermore, based on fuzzy inference system (FIS), the predicted model of the degradation degree for lithium-ion battery was built and its output was defined as the degenerate coefficient [Formula: see text], [Formula: see text]. Finally, by learning, training and simulating, the FIS model has been validated to be reliable and applicable in prediction on the degradation degree of lithium-ion battery. The simulation results show that the degradation degree of lithium-ion battery is more serious when [Formula: see text] is closer to 1, and the degradation degree is lighter when [Formula: see text] is closer to 0.

Study on degenerate coefficient and degeneration evaluation of lithium-ion battery

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740092
Author(s):  
Bei Li ◽  
Xiaopeng Li
Keyword(s):  
Lithium Ion Battery ◽  
Model Building ◽  
Fuzzy Inference ◽  
Lithium Ion ◽  
Open Circuit ◽  
Characteristic Parameters ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Degradation Degree ◽  
Internal And External Factors

Some characteristic parameters were epurated in this paper by analyzing internal and external factors of the degradation degree of lithium-ion battery. These characteristic parameters include open circuit voltage (OCV), state of charge (SOC) and ambient temperature. The degradation degree was evaluated by discrete degree of the array, which is composed of the above parameters. The epurated parameters were verified through adaptive neuro-fuzzy inference system (ANFIS) model building. The expression of degradation coefficient was finally determined. The simulation results show that the expression is reasonable and precise to describe the degradation degree.

scholarly journals Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2269
Author(s):  
Ahmed F. Bendary ◽  
Almoataz Y. Abdelaziz ◽  
Mohamed M. Ismail ◽  
Karar Mahmoud ◽  
Matti Lehtonen ◽  
...  
Keyword(s):  
Fault Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection Methods ◽  
Photovoltaic Systems ◽  
Inference System ◽  
Photovoltaic Array ◽  
Neuro Fuzzy ◽  
Effective Manner ◽  
Simulation Results

In the last few decades, photovoltaics have contributed deeply to electric power networks due to their economic and technical benefits. Typically, photovoltaic systems are widely used and implemented in many fields like electric vehicles, homes, and satellites. One of the biggest problems that face the relatability and stability of the electrical power system is the loss of one of the photovoltaic modules. In other words, fault detection methods designed for photovoltaic systems are required to not only diagnose but also clear such undesirable faults to improve the reliability and efficiency of solar farms. Accordingly, the loss of any module leads to a decrease in the efficiency of the overall system. To avoid this issue, this paper proposes an optimum solution for fault finding, tracking, and clearing in an effective manner. Specifically, this proposed approach is done by developing one of the most promising techniques of artificial intelligence called the adaptive neuro-fuzzy inference system. The proposed fault detection approach is based on associating the actual measured values of current and voltage with respect to the trained historical values for this parameter while considering the ambient changes in conditions including irradiation and temperature. Two adaptive neuro-fuzzy inference system-based controllers are proposed: (1) the first one is utilized to detect the faulted string and (2) the other one is utilized for detecting the exact faulted group in the photovoltaic array. The utilized model was installed using a configuration of 4 × 4 photovoltaic arrays that are connected through several switches, besides four ammeters and four voltmeters. This study is implemented using MATLAB/Simulink and the simulation results are presented to show the validity of the proposed technique. The simulation results demonstrate the innovation of this study while proving the effective and high performance of the proposed adaptive neuro-fuzzy inference system-based approach in fault tracking, detection, clearing, and rearrangement for practical photovoltaic systems.

scholarly journals The Effect of External Factors on Consumption Electricity Loads Forecasting using Fuzzy Takagi-Sugeno Kang

MATICS ◽  
2017 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Gayatri Dwi Santika ◽  
Wayan F Mahmudy
Keyword(s):  
Fuzzy Inference System ◽  
Historical Data ◽  
Fuzzy Inference ◽  
External Factors ◽  
Electrical Load ◽  
Inference System ◽  
Takagi Sugeno

<strong>This study applied Fuzzy Inference System Sugeno to forecast electrical load by considering the external factors. To see the accuracy of forecasting using Fuzzy Inference System Sugeno, then a comparison between the forecasting results of Fuzzy Inference System Sugeno using historical data with Fuzzy Inference System Sugeno using external factors was done. By using external factors method, resulted the smallest RMSE of 0762 and using historical data obtained error (RMSE) of 1028. The results of the study came to the conclusion that Fuzzy Inference System Sugeno method using external factors to forecast the consumption of electrical load gives a better result than Fuzzy Inference System Sugeno using only historical data.</strong>

scholarly journals Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm

2019 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Dasong Wang ◽  
Feng Yang ◽  
Lin Gan ◽  
Yuliang Li
Keyword(s):  
Lithium Ion Battery ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Lithium Ion ◽  
Inference System ◽  
Fuzzy C Means ◽  
Power Battery ◽  
Fuzzy C Means Clustering ◽  
Scientists And Engineers

Following the widespread and large-scale application of power lithium ion battery, State of Function (SOF) estimation technology of power lithium ion batteries has gained an increasing amount of attention from both scientists and engineers. During the lifetime of the power lithium ion battery, SOF reflects the maximum instantaneous output power of the battery. When discarded, it is able to show the degree of performance degradation of the power battery when also taken as a performance evaluation parameter. In this paper, the variables closely related to SOF have been selected to conduct the fuzzy inference system, which is optimized by the fuzzy c-means clustering algorithm, to estimate the SOF of the power lithium ion battery, whose relations can be proved by experimental data. Our simulation results and experimental results demonstrate the feasibility and advantages of the estimation strategy.

scholarly journals Adaptive neuro-fuzzy inference system based evolving fault locator for double circuit transmission lines

2020 ◽  
Vol 9 (3) ◽  
pp. 448 ◽  
Author(s):  
A Naresh Kumar ◽  
P Sridhar ◽  
T Anil Kumar ◽  
T Ravi Babu ◽  
V Chandra Jagan Mohan
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Fuzzy Inference System ◽  
Fault Location ◽  
Fuzzy Inference ◽  
Cost Effective ◽  
Inference System ◽  
Protection Method ◽  
Neuro Fuzzy ◽  
Simulation Results

<p>Evolving faults are starting in one phase of circuit and spreading to other phases after some time. There has not been a suitable method for locating evolving faults in double circuit transmission line until now. In this paper, a novel method for locating different types of evolving faults occurring in double circuit transmission line is proposed by considering adaptive neuro-fuzzy inference system. The fundamental current and voltage magnitudes are specified as inputs to the proposed method. The simulation results using MATLAB verify the effectiveness and correctness of the protection method. Simulation results show the robustness of the method against different fault locations, resistances, time intervals, and all evolving fault types. Moreover, the proposed method yields satisfactory performance against percentage errors and fault location line parameters. The proposed method is easy to implement and cost-effective for new and existing double circuit transmission line installation</p>

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