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

School of Automation Engineering, University of Electronic Science and Technology of China, Qingshuihe Campus of UESTC, No.2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, China
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World Electr. Veh. J. 2019, 10(1), 1; https://doi.org/10.3390/wevj10010001
Received: 8 November 2018 / Revised: 10 December 2018 / Accepted: 17 December 2018 / Published: 3 January 2019
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Abstract

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. View Full-Text
Keywords: power battery; SOF; fuzzy prediction; fuzzy c-means clustering power battery; SOF; fuzzy prediction; fuzzy c-means clustering
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Wang, D.; Yang, F.; Gan, L.; Li, Y. Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm. World Electr. Veh. J. 2019, 10, 1.

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