Development of a Hybrid Adaptive Neuro-fuzzy Inference System with Coulomb-Counting State-of-Charge Estimator for Lithium–Sulphur Battery
نویسندگان
چکیده
Abstract This study presents the development of an improved state charge (SOC) estimation technique for lithium–sulphur (Li–S) batteries. is a promising technology with advantages in comparison existing lithium-ion (Li-ion) batteries such as lower production cost and higher energy density. In this study, state-of-the-art Li–S prototype cell subjected to experimental tests, which are carried out replicate real-life duty cycles. A system identification then used on test results parameterize equivalent circuit model cell. The demonstrate unique features cell’s voltage-SOC ohmic resistance-SOC curves, large flat region observed middle SOC range. Due this, voltage resistance parameters not sufficient accurately estimate under various initial conditions. To solve problem, forgetting factor recursive least squares (FFRLS) used, yielding four train adaptive neuro-fuzzy inference (ANFIS). Sugeno-type fuzzy inputs one output (SOC), totalling 375 rules. Each Gaussian-type membership functions while linear type. network combined coulomb-counting method obtain hybrid estimator that can conditions maximum error 1.64%, outperforms methods battery estimation.
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ژورنال
عنوان ژورنال: International Journal of Fuzzy Systems
سال: 2022
ISSN: ['2199-3211', '1562-2479']
DOI: https://doi.org/10.1007/s40815-022-01403-y