A Novel Data-Driven Estimation Method for State-of-Charge Estimation of Li-Ion Batteries

Suwei Zhai, Wenyun Li, Cheng Wang and Yundi Chu
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Suwei Zhai: Electric Power Research Institute of China Southern Power Grid Yunnan Power Grid Co., Ltd., Kunming 650217, China
Wenyun Li: Yunnan Power Dispatching Control Center of China Southern Power Grid, Kunming 650011, China
Cheng Wang: College of IOT Engineering, Hohai University, Nanjing 210098, China
Yundi Chu: College of IOT Engineering, Hohai University, Nanjing 210098, China

Energies, 2022, vol. 15, issue 9, 1-17

Abstract: With the increasing proportion of Li-ion batteries in energy structures, studies on the estimation of the state of charge (SOC) of Li-ion batteries, which can effectively ensure the safety and stability of Li-ion batteries, have gained much attention. In this paper, a new data-driven method named the probabilistic threshold compensation fuzzy neural network (PTCFNN) is proposed to estimate the SOC of Li-ion batteries. Compared with other traditional methods that need to build complex battery models, the PTCFNN only needs data learning to obtain nonlinear mapping relationships inside Li-ion batteries. In order to avoid the local optimal value problem of traditional BP neural networks and the fixed reasoning mechanism of traditional fuzzy neural networks, the PTCFNN combines the advantages of a probabilistic fuzzy neural network and a compensation fuzzy neural network so as to improve the learning convergence speed and optimize the fuzzy reasoning mechanism. Finally, in order to verify the estimation performance of the PTCFNN, a 18650-20R Li-ion battery was used to carry out the estimation test. The results show that the mean absolute error and mean square error are very small under the conditions of a low-current test and dynamic-current test, and the overall estimation error is less than 1%, which further indicates that this method has good estimation ability.

Keywords: Li-ion batteries; state of charge; fuzzy neural network; data-driven (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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