State of health estimation of Li-ion battery based on dual calibration of internal resistance increasing and capacity fading

doi:10.19799/j.cnki.2095-4239.2020.0395

Abstract

Abstract:

The management of the state of health (SOH) and life prediction of Li-ion batteries are of great significance to promote their wide application. In this paper, on the basis of the situation of application of Li-ion batteries, to remedy the real-time estimation difficulty and low precision under various working conditions, a ternary Li-ion battery is taken as the research object, a second-order RC equivalent circuit model is established to characterize the operating characteristics of the battery, and the performance of the Li-ion battery is studied and analyzed on the basis of the experiments under various working conditions. The changes in the state of health were analyzed from two directions: internal resistance increase and capacity fading. The effect of the state of charge (SOC) on internal resistance is considered. The SOC is calibrated under discharge conditions, and the change of internal resistance is analyzed from 0 to 1 s and 1 to 10 s, respectively. We established the corresponding calculation formula of the evaluation method on the basis of the internal resistance increase of the Li-ion battery and determined the changes of state of health under different initial SOCs in two time ranges. The temperature is taken as a parameter, and the measurement interval is extended to reflect the capacity fading at different temperatures more accurately by multiple full charging and discharging experiments. We also established the corresponding mathematical formula of the state of health on the basis of capacity fading for the Li-ion battery and obtained the changes in the battery's state of health at different temperatures. The experimental results show that within 0—1 s, there is no relationship between the state of health of the Li-ion battery and the initial SOC. Within 1—10 s, the decline rate of the state of health is inversely proportional to the initial SOC. The complete discharging experiments at different temperatures show that the Li-ion battery has the best health condition at 25℃, so the test battery should be operated at this temperature as far as possible. It is indicated that the second-order RC equivalent circuit model can estimate the state of health of Li-ion batteries very well; the convergence rate is fast, and the tracking effect is good. The state of health estimation errors based on internal resistance increase and capacity fading are controlled within 1.0% and 0.8%, respectively. Improving the state of health estimation method helps promote the applications of Li-ion batteries.

Key words: health management of Li-ion battery, internal-resistance increasing, capacity fading, second-order RC equivalent circuit model, state of health estimation

CLC Number:

TM 911

Pu REN, Shunli WANG, Mingfang HE, Yongcun FAN, Wen CAO, Wei XIE. State of health estimation of Li-ion battery based on dual calibration of internal resistance increasing and capacity fading[J]. Energy Storage Science and Technology, 2021, 10(2): 738-743.

Figures/Tables 6

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Table 1

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References 22

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SOC	a	b	c	I_L/A	τ₁	τ₂
0.8	3.732	0.0638	0.02238	68.4771	27.12	0.4055
0.7	3.631	0.07757	0.02208	68.474	32.34	0.4073
0.6	3.528	0.05834	0.02185	68.4678	25.65	0.4103
0.5	3.456	0.05124	0.02161	68.4709	30.33	0.4025
0.4	3.422	0.01984	0.04562	68.4709	26.85	0.4245
0.3	3.396	0.05502	0.02052	68.474	32.02	0.4251

SOC	U_OC/V	R₀/Ω	R₁/Ω	C₁/F	R₂/Ω	C₂/F
0.8	3.9368	0.003151419	0.000931698	29108.13404	0.000326825	1240.726723
0.7	3.832339457	0.003101023	0.001132839	28547.75248	0.000322458	1263.109611
0.6	3.7252	0.003040845	0.000852079	30102.82945	0.000319128	1285.690542
0.5	3.6527	0.003004196	0.000748347	40529.32078	0.000315609	1275.314079
0.4	3.61845	0.002985940	0.000289758	1465.01497	0.000666268	40299.07201
0.3	3.59065	0.002974122	0.000803517	39849.82697	0.000299676	1418.533012

SOC	0～1 s内拟合曲线参数			1～10 s内拟合曲线参数
SOC	a	b	R²	a	b	R²
0.3	5.2571	25.075	0.9950	1.4455	32.811	0.8845
0.4	5.2552	24.923	0.9919	1.7110	31.854	0.8963
0.5	5.2593	24.995	0.9892	2.1564	31.015	0.9292
0.6	5.2547	24.998	0.9938	2.7394	29.518	0.9687
0.7	5.2557	24.954	0.9934	3.5778	27.510	0.9949
0.8	5.2597	24.912	0.9922	4.9394	22.249	0.9872

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