An estimation method for state of health of sodium-ion batteries

doi:10.3969/j.issn.2097-0706.2024.07.009

Abstract

Abstract:

Sodium ion batteries are promising energy storage devices due to their economy and abundant material sources.An accurate assessment on a battery's state of health（SOH） is essential to ensure its efficient and safe operation. Integrating the techniques of Recurrent Neural Networks（RNN） and Extended Kalman Filtering（EKF），a novel framework for SOH estimation is proposed.The RNN， with its capability to process time series data，offers a sound support for the SOH estimation，while the EKF ensures the robustness of state estimation. Through experimental validation on three sodium-ion batteries，the proposed method demonstrates outstanding estimating performances，with an average absolute error of less than 1.79%，a root mean square error of less than 1.38%，and a model fitting up to 96.28%. This research not only provides an efficient approach for the SOH estimation of sodium-ion batteries，but also offers valuable insights for battery management and maintenance in practical applications.

Key words: sodium-ion battery, state of health, recurrent neural network, extended Kalman filtering, battery management system

CLC Number:

TK01:TM912.1

SUN Wenjie, YANG Zhile, GUO Yuanjun, YAO Wenjiao, XU Huan, ZHOU Bowen. An estimation method for state of health of sodium-ion batteries[J]. Integrated Intelligent Energy, 2024, 46(7): 74-80.

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算法	R_MSE/%	M_AE/%	R²/%
GRU	30.405 9	26.341 4	0.000 0
LSTM	40.642 3	36.049 5	0.000 0
BiGRU	26.882 4	23.105 7	0.000 0
BiLSTM	26.978 2	24.262 9	0.000 0
RNN	2.625 4	2.292 0	87.469 8
RNN-EKF	1.797 6	1.386 6	94.126 1

算法	R_MSE/%	M_AE/%	R²/%
GRU	5.881 5	5.222 3	38.064 5
LSTM	16.661 4	15.244 1	0.000 0
BiGRU	5.457 5	4.874 2	46.671 8
BiLSTM	11.026 4	10.294 2	0.000 0
RNN	1.406 0	1.159 0	96.460 5
RNN-EKF	1.313 4	1.074 6	96.911 3

算法	R_MSE/%	M_AE/%	R²/%
GRU	9.434 1	15.331 6	0.000 0
LSTM	31.435 5	26.751 1	0.000 0
BiGRU	28.925 8	23.972 0	0.000 0
BiLSTM	37.751 9	33.864 2	0.000 0
RNN	2.555 4	2.149 1	77.338 4
RNN-EKF	0.802 1	0.702 1	97.767 2