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.

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.

The establishment and analysis of a state of charge model for lithium ion batteries

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840060 ◽  
Author(s):  
Lin Yu ◽  
Ming-Fang Liu
Keyword(s):  
Fuzzy Inference ◽  
Lithium Ion ◽  
State Of Charge ◽  
Prediction System ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Charge Model ◽  
Battery Packs ◽  
Different Temperatures ◽  
Residual Capacity

Based on the rebound voltage data which was measured under different temperatures for lithium ion battery, a residual capacity prediction model used adaptive neuro-fuzzy inference system (ANFIS) was established. The rebound voltage and temperature were the input of prediction system, and the output was residual capacity. A large number of experimental data were used to train and verify the model. The reliability and applicability of the model were proven by applying it to predict and verify the residual capacity of different battery packs.

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.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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