State-of-health prediction for lithium batteries based on feature decoupling and heterogeneous stacking ensemble learning framework

Abstract

Accurate state-of-health (SOH) prediction of lithium batteries is vital for guaranteeing the safe operation of battery systems and extending their service life. However, previous data-driven models were usually established based on ideal laboratory environments, which would severely restrict their generalization ability in real-world applications. This paper develops an SOH prediction method based on feature decoupling and a heterogeneous stacking ensemble learning framework (HSELF). First, the validation dataset was constructed by using accelerated cycle aging data with three levels of gradient noise. Second, features based on incremental capacity analysis (ICA) were extracted from a selected charging voltage interval to capture underlying battery degradation characteristics. Then, a feature decoupling strategy based on noise robustness decay rate (NRDR) and the corresponding differentiated feature input spaces were constructed for the heterogeneous stacking model. Additionally, a nested inner leave-one-battery-out cross-validation (LOBOCV) strategy was developed to achieve an unbiased evaluation of generalization under small-sample conditions. Finally, the ridge regression method was adopted as the meta-learner to integrate the outputs of the base models in the HSELF, leveraging their complementary strengths for mitigating prediction errors. Experimental results based on the LISHEN dataset successfully demonstrated the effectiveness of the developed SOH prediction method based on feature decoupling and HSELF. Under noise-free and typical industrial noise (𝜎≤10 mV ) conditions, the accuracy of the developed SOH prediction method would be significantly superior to that of individual base models. Furthermore, although the extreme noise ( 𝜎= 20 mV ) causes severe performance degradation with individual base models, the developed SOH prediction method can consistently maintain high prediction stability (𝑅² > 0.93). In addition, experimental results based on an additional HNEI dataset also demonstrate that the developed SOH prediction method can achieve an effective balance among prediction accuracy, noise robustness, and cross-dataset robustness.

Keywords

lithium batteries / state-of-health prediction / incremental capacity analysis / heterogeneous stacking ensemble learning framework / feature decoupling

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