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.

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 Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

2014 ◽  
Vol XL-2/W3 ◽  
pp. 19-23
Author(s):  
P. Akhavan ◽  
M. Karimi ◽  
P. Pahlavani
Keyword(s):  
Data Mining ◽  
Cutaneous Leishmaniasis ◽  
Fuzzy Inference System ◽  
Prevalence Rate ◽  
Fuzzy Systems ◽  
Fuzzy Inference ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering

Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

Forecasting East Asian Tourist Arrivals to Thailand with Adaptive Neuro-Fuzzy Inference System

2019 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Chawalsak Phetchanchai ◽  
Chuthawuth Chantaramalee ◽  
Napatsarun Chatchawalanont ◽  
Piyapong Phatcha
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
East Asian ◽  
Time Lag ◽  
Training Dataset ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering ◽  
Takagi Sugeno

Objective - This research aims to propose the approach of forecasting tourist arrivals to Thailand. Methodology/Technique – Adaptive Neuro-Fuzzy Inference System (ANFIS) was used as our forecasting method by using fuzzy C-means clustering as a technique for the partitioning training dataset Findings - The appropriate parameter of time lag was found for each dataset of East Asian tourist arrivals to Thailand. Novelty - The forecasting procedure with the appropriate parameter of time lag was represented our work as a novelty idea. Type of Paper: Empirical. Keywords: Tourist arrivals forecasting, East Asian countries, adaptive neuro-fuzzy inference system, fuzzy C-means clustering, Takagi–Sugeno fuzzy inference system.

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.

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