Stacking integrated learning model via ELM and GRU with mixture correntropy loss for robust state of health estimation of lithium-ion batteries

Xue, Jingsong; Ma, Wentao; Feng, Xiaoyang; Guo, Peng; Guo, Yaosong; Hu, Xianzhi; Chen, Badong

doi:10.1016/j.energy.2023.129279

Cited by 11 publications

(1 citation statement)

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“…Domestic and international scholars have proposed various methods for estimating the SOH of lithium-ion batteries, which can generally be divided into two categories: model-based methods [11][12][13][14][15][16][17][18] and data-driven methods [19][20][21][22][23][24][25][26]. Model-based methods involve establishing appropriate equivalent models of lithium-ion batteries to simulate average value, and standard deviation, are extracted.…”

Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery SOH Estimation Method Based on Multi-Feature and CNN-BiLSTM-MHA

Zhou,

Zhang,

Zhang

et al. 2024

WEVJ

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Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long Short-Term Memory–Multi-head Attention (CNN-BiLSTM-MHA)-based lithium-ion battery SOH estimation method is proposed in this paper. First, the voltage, energy, and temperature data of the battery in the constant current charging phase are measured. Then, based on the voltage and energy data, the incremental energy analysis (IEA) is performed to calculate the incremental energy (IE) curve. The IE curve features including IE, peak value, average value, and standard deviation are extracted and combined with the thermal features of the battery to form a complete multi-feature sequence. A CNN-BiLSTM-MHA model is set up to map the features to the battery SOH. Experiments were conducted using batteries with different charging currents, and the results showed that even if the nonlinearity of battery SOH degradation is significant, this method can still achieve a fast and accurate estimation of the battery SOH. The Mean Absolute Error (MAE) is 0.1982%, 0.1873%, 0.1652%, and 0.1968%, and the Root-Mean-Square Error (RMSE) is 0.2921%, 0.2997%, 0.2130%, and 0.2625%, respectively. The average Coefficient of Determination (R2) is above 96%. Compared to the BiLSTM model, the training time is reduced by an average of about 36%.

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