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A novel Co-estimation framework of state-of-charge, state-of-power and capacity for lithium-ion batteries using multi-parameters fusion method by Kuo Li; Xiao Gao; Caixia Liu; Chun Chang; Xiaoyu Li is a Engineering article available to read on EtoBox.

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A battery management system can intelligently manage and maintain battery systems by effectively estimating and predicting battery internal states. Owing to battery nonlinear characteristics related to various influence factors, the estimation of battery internal states should consider the available capacity and ohmic internal resistance. This paper proposes a co-estimation framework for state of charge (SOC), state of power (SOP) and battery available capacity. Firstly, the first-order equivalent model method is used to identify the battery parameters by recursive least squares algorithm with variable forgetting factor, and the SOC-OCV curve of the battery is obtained by combining the ampere-time integration method. Secondly, three Kalman filters are utilized to estimate battery SOCs and the maximum available capacity and internal resistance are estimated by a forgetting factor recursive least square algorithm. Then peak current and power are estimated under the composite constraints of the estimated capacity and internal resistance. Finally, the experimental data are collected at temperatures 25 °C and 40 °C to verify and analyze the proposed method. The results of battery state

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Author

Kuo Li; Xiao Gao; Caixia Liu; Chun Chang; Xiaoyu Li

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)