Lithium battery SOH prediction based on Frequency-Enhanced Cross variable for Short-Term Dependency Recognition framework

Abstract Accurate and stable predictions of the state-of-health (SOH) of lithium-ion batteries are essential for effective battery management and extending battery lifespan. Two major issues exist in current lithium battery capacity prediction models. First the original data captured from temperature, voltage, and current sensors contains a large number of noises, which negatively impacts prediction accuracy. Second, the SOH of lithium-ion batteries is significantly influenced by factors such as current temperature, charge and discharge rates, and voltage. These rapid changing factors can be captured more effectively using short-term dependence rather than long-term dependence. However, the short-term dependence of capacity degradation is not fully captured, and the coupling relationships among multivariable sequences, along with the inaccurate capacity predictions resulting from frequency information, are not adequately addressed. In this paper, we propose a frequency-domain enhanced trans-variate short-term dependence recognition framework, FE-STDR, to solve these two problems and predict the SOH of lithium batteries. The FE-STDR framework comprises two modules including Stacked Denoised Autoencoder (SDAE) and FEformer. SDAE module removes noise captured during the original battery data acquisition and capacity cycles, automatically extracting high-level features to capture complex battery state patterns through its multi-layer denoising structures. The FEformer module introduces a Frequency Enhanced Channel Attention Mechanism to integrate frequency information into the framework while considering short-term frequency dependence in lithium battery data. The results indicate that the RMSE of the proposed framework is reduced by 6.53%, 11.78%, and 7.34%, respectively, compared to the baseline models: LSTM, Transformer, and AE-GRU. The prediction accuracy proves that the proposed FE-STDR is superior, enabling accurate forecasting of the degradation trajectory of lithium battery SOH.