A new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference system

2004 ◽  
Vol 45 (11-12) ◽  
pp. 1681-1692 ◽  
Author(s):  
K.T. Chau ◽  
K.C. Wu ◽  
C.C. Chan
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Lithium Ion ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Battery Capacity

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.

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