A Flexible Online State of Health Estimation Approach for Lithium -ion Battery

Linghu, Jinqing; Kang, Longyun; Luo, Xin; Lu, Chusheng; Lin, Hongye; Zhao, Zhongyang

doi:10.1109/ipemc-ecceasia48364.2020.9367785

Cited by 1 publication

(1 citation statement)

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“…NCR18650GA has the characteristics of high energy density, no memory effect, and flat discharge voltage. The typical capacity of the NCR18650 is 3450 mAh, while the nominal voltage, discharging end voltage and charging end voltage are 3.6, 2.5, and 4.2 V, respectively [39]. The maximum continuous discharge current is 10 A.…”

Section: Experiments Platformmentioning

confidence: 99%

Online State-of-Health Estimation of Lithium-Ion Battery Based on Incremental Capacity Curve and BP Neural Network

Lin

Kang

Xie³

et al. 2022

Batteries

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Lithium-ion batteries (LIBs) have been widely used in various fields. In order to ensure the safety of LIBs, it is necessary to accurately estimate of the state of health (SOH) of the LIBs. This paper proposes a SOH hybrid estimation method based on incremental capacity (IC) curve and back-propagation neural network (BPNN). The voltage and current data of the LIB during the constant current (CC) charging process are used to convert into IC curves. Taking into account the incompleteness of the actual charging process, this paper divides the IC curve into multiple voltage segments for SOH prediction. Corresponding BP neural network is established in multiple voltage segments. The experiment divides the LIBs into five groups to carry out the aging experiment under different discharge conditions. Aging experiment data are used to establish the non-linear relationship between the decline of SOH and the change of IC curve by BP neural network. Experimental results show that in all voltage segments, the maximum mean absolute error does not exceed 2%. The SOH estimation method proposed in this research makes it possible to embed the SOH estimation function in battery management system (BMS), and can realize high-precision SOH online estimation.

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Section: Experiments Platformmentioning

confidence: 99%