Fuzzy Inference System for Large Scale Lithium-Ion Battery Management Systems

2019 ◽  
Author(s):  
Timothy Transue ◽  
Antonio Maldonado ◽  
Taylor Santre
Keyword(s):  
Lithium Ion Battery ◽  
Fuzzy Inference System ◽  
Large Scale ◽  
Fuzzy Inference ◽  
Lithium Ion ◽  
Management Systems ◽  
Battery Management ◽  
Inference System ◽  
Battery Management Systems

scholarly journals Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems

Energies ◽  
2018 ◽  
Vol 11 (6) ◽  
pp. 1490 ◽  
Author(s):  
Manoj Mathew ◽  
Stefan Janhunen ◽  
Mahir Rashid ◽  
Frank Long ◽  
Michael Fowler
Keyword(s):  
Comparative Analysis ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Management Systems ◽  
Battery Management ◽  
Estimation Techniques ◽  
Battery Management Systems

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.

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.

scholarly journals Development of an Experimental Testbed for Research in Lithium-Ion Battery Management Systems

Energies ◽  
2013 ◽  
Vol 6 (10) ◽  
pp. 5231-5258 ◽  
Author(s):  
Nima Lotfi ◽  
Poria Fajri ◽  
Samuel Novosad ◽  
Jack Savage ◽  
Robert Landers ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Management Systems ◽  
Battery Management ◽  
Battery Management Systems ◽  
Experimental Testbed
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