Predicting the battery’s end-of-life (EOL) with uncertainty quantification is critical for ensuring system safety and reliability. This paper presents a hybrid framework for battery EOL prediction and its uncertainty assessment based on Gaussian process regression (GPR) and Kalman filter (KF). First, a KF-based empirical-model-free state tracking phase is applied for the available partial battery degradation data. Then, the original time series forecasting problem of degradation curves is converted to the prediction of the virtual degradation rate and acceleration. Next, the prediction of the virtual degradation rate and acceleration is executed by the iterative GPR multi-step ahead prediction strategy with moving sliding windows. Finally, the uncertainty assessment is carried out based on the sliding window length determination process. The effectiveness of our proposed method is validated on the open-source lithium-ion battery degradation dataset from the University of Oxford. Extensive EOL prediction tests have been carried out from 40% (early-stage), 60% (middle-stage), and 80% (late-stage) of the dataset, respectively. Compared with the popular EOL prediction method within particle filter framework, the predicted mean EOL cycle by our method is closer to the true value with a smaller range of prediction uncertainty.

Digital twins are now being widely applied in fault and lifetime prediction of complex systems. In this paper, a digital twin for fuel cell degradation prediction via transfer learning method is proposed. As the fuel cell degradation is very susceptible to operation conditions, a multi-input data-driven behaviour model of fuel cell degradation is constructed based on a connected convolutional neural network and long short-term memory network to capture both spatial and temporal characteristics hidden in the data. Transfer learning method is applied in order to leverage the knowledge from historical datasets to reliably predict the fuel cell degradation in real-time operation, especially in the early stage. The developed degradation prediction digital twin is cross-validated using two fuel cell ageing experiment datasets, and the results showcase the effectiveness and generalizability of the proposed approach. This paper contributes to developing an early-stage fuel cell degradation prediction digital twin, which is tolerant to different degradation patterns and can achieve real-time degradation prediction.

As an important property and distinct characteristic of different lithium‐ion batteries, open‐circuit‐voltage (OCV) online estimation can provide useful information for battery monitoring and fault diagnosis. However, studies dedicated to battery OCV estimation are not as much as the research efforts on state‐of‐charge determination and parameter identification such as capacity and resistance. Hence, a general discussion for selecting the battery OCV estimation algorithm is proposed in this study. To this end, modelling process of extended state‐space model and autoregressive exogenous model is presented in detail. Four estimation algorithms, namely, Luenberger observer, Kalman filter, recursive least‐square with forgetting factor and recursive least‐square with variable forgetting factor are selected and compared in terms of estimation accuracy, computational cost, parameter tuning and robustness to parameter variations. Based on real battery cell parameters and environmental conditions, simulation results have shown that even if they are less robust to model uncertainty, observer‐based methods exhibit better estimation performances than regression‐based ones.

Data-driven state-of-health (SOH) prediction is increasingly critical for the effective management of lithium-ion batteries; however, challenges remain in practical applications. Traditional methods that rely on a single health indicator often fail to capture the complexity and multi-dimensional nature of battery performance changes. To address these limitations, this paper presents a novel Transformer-based approach for accurate SOH prediction. The correlation between various measured and computed features extracted from battery charge/discharge curves and their impact on battery performance degradation are investigated using Pearson correlation coefficients. Three strongly correlated features are identified as multiple input variables for the Transformer framework. The effectiveness of this Transformer-based SOH prediction method is demonstrated using public datasets, revealing that predictions for internal resistance and capacity closely align with actual values, with most RMSE values falling below 0.01. Furthermore, validation with an additional laboratory dataset confirms the accuracy and adaptability of our proposed approach, highlighting its potential to enhance SOH prediction in real-world applications.