State of health prediction for sodium-ion batteries

doi:10.19799/j.cnki.2095-4239.2021.0036

Abstract

Abstract:

Sodium-ion batteries (SIBs) show promising application prospect in large-scale energy storage, due to the abundant and low-cost sodium resources. Most of the research focuses on the development of new SIB materials such as electrodes and electrolytes. Engineering manufacturing technologies and demonstration applications are still in the exploration stage. To ensure high safety, long life, and high efficiency operation, the battery management systems (BMSs) based on the characteristics of SIBs need to be developed. Accurate state of health (SOH) prediction is one of the core functions of BMS, and single-step-ahead and multi-step-ahead SOH prediction are important for the estimation of state of charge and the prediction of remaining useful life (RUL), respectively. Compared to lithium-ion batteries, SIBs have similar operation mechanism, but the larger sodium ions result in more complicated battery characteristics and aging mechanism, which may make it difficult for accurate SOH prediction for the full SIBs. Based on the SOH time series, a double-exponential model-based Particle filter (DEM-PF) method and a wavelet-analysis-based Gaussian process regression (WA-GPR) method are proposed. In the DEM-PF method, the DEM is utilized to model SOH time series. The PF is used to update the model parameters. In the WA-GPR method, WA is used to decouple the global degradation trend and local capacity regeneration and fluctuations of SOH time series. The GPR with time index input is used to prediction the global degradation trend. The GPR with lag vector input realizes the autoregression of the local capacity regeneration and fluctuation. The two methods are validated and compared in the 1 C charge/discharge aging test of a 1 A·h pouch-type SIB. The results indicates that the WA-GPR method shows better accuracy and stability both in the one-step-ahead SOH and RUL prediction, with the prediction root mean square error of 0.8% for one-step-ahead SOH and minimum error of 3 cycles for RUL.

Key words: sodium-ion battery, state of health, particle filter, Gaussian process regression, Wavelet analysis

CLC Number:

TM 912

Yifeng FENG, Jiani SHEN, Haiying CHE, Zifeng MA, Yijun HE, Wen TAN, Qingheng YANG. State of health prediction for sodium-ion batteries[J]. Energy Storage Science and Technology, 2021, 10(4): 1407-1415.

Figures/Tables 8

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Table 1

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预测起点	方法	真实RUL	预测RUL	RUL误差	SOH预测RMSE/%
100	DEM-PF	535	475	60	6.3
100	WA-GPR	535	500	35	3.8
200	DEM-PF	435	391	44	5.0
200	WA-GPR	435	417	18	3.2
300	DEM-PF	335	321	14	4.1
300	WA-GPR	335	332	3	2.5

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