SOC estimation of aging lithium-ion battery based on a migration model

doi:10.19799/j.cnki.2095-4239.2020.0288

Abstract

Abstract:

In the process of estimating the state of charge (SOC) for lithium-ion batteries, the estimation results can be significantly influenced by a reduction of available capacity and changes in the internal parameters of aging batteries. To address this problem, this paper proposes a novel method for estimating the SOC of aging lithium-ion batteries based on the migration model, by regarding the aging of batteries as an uncertain factor that affects the estimation of the SOC. First, with the second-order resistance-capacitance (RC) equivalent battery model at the original state as an initial battery model, recursive least squares (RLS) and polynomial fitting are employed to extract the relationship between parameters of the initial model and the SOC, which is then linearly migrated to obtain a state equation of the migration model. Second, migration factors of the model are updated in the actual running of batteries with the risk sensitive particle filter algorithm (RSPF). Finally, a precise estimation of the SOC is attained in combination with low-pass filters. Based on four sets of Urban Dynamometer Driving Schedule (UDDS) data at different degrees of aging, the migration model algorithm was verified and compared with the extended Kalman filter (EKF) and the adaptive extended Kalman filter (AEKF) algorithms. The experimental studies indicated that the model proposed in this paper is precise over a range battery ages and is effective, with a root-mean-square error (RMSE) of the SOC calculation less than 1.04%. The study can help to promote the application of the migration model in the estimation of the SOC of aging lithium-ion batteries, and can provide guidance and a reference for the estimation of the SOC of electric vehicle batteries throughout their lifecycles.

Key words: aging lithium-ion batteries, state of charge, migration model, risk sensitive particle filter

CLC Number:

TM 911

Zheng CHEN, Guangda ZHAO, Shiquan SHEN, Xing SHU, Jiangwei SHEN. SOC estimation of aging lithium-ion battery based on a migration model[J]. Energy Storage Science and Technology, 2021, 10(1): 326-334.

Figures/Tables 12

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SOH/%	RMSE/mV	MAE/mV	MAPE/mV
100	13.3	3.3	18.5
99	13.8	3.2	14
93	13.8	3.2	17.2
85	14.2	3.9	26.8

SOH/%	RMSE/%	MAE/%	MAPE/%
100	1.04	0.82	3.33
99	0.97	0.80	2.30
93	0.64	0.49	2.57
85	0.95	0.72	3.04

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