Joint evaluation and prediction of SOH and RUL for lithium batteries based on a GBLS booster multi-task model

From the perspective of safe electric vehicle operation, accurately assessing the state of health (SOH) and remaining useful life (RUL) of lithium batteries holds paramount importance. This paper introduces a novel multi-task learning data-driven model named GBLS Booster, focusing on the joint estimation and prediction of SOH and RUL. GBLS Booster integrates the strengths of GBLS, offering reduced computation, swift computing speed, and harnesses the powerful feature extraction capabilities of the CNN-Transformers algorithm-based Booster. Additionally, the Tree-structured Parzen Estimator (TPE) algorithm is applied to optimize the model. In this study, 10 healthy indicators (HIs) are devised to capture variations in battery SOH. These HIs are derived from readily available sensor data, encompassing current, voltage, and temperature information. The random forest method (RF) is employed to further refine features and minimize data dimensions. Concerning the RUL prediction, the capacity data is often plagued by significant noise. To address this challenge, the complete empirical mode decomposition (CEEMDAN) method is employed for noise reduction decomposition, followed by the utilization of the Pearson correlation coefficient to eliminate noisy data points. The proposed method is rigorously evaluated using the NASA dataset and CLACE dataset for modeling simulation and verification. Comparative analysis with other algorithms is conducted. The results demonstrate the superior performance of the proposed model, showcasing exceptional accuracy (with a minimum Mean Absolute Percentage Error (MAPE) of 0.3348 % for SOH and a minimum Relative Error (RE) of 0.01 % for RUL), robustness, and generalization capabilities.