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Lithium-ion batteries are widely utilized in numerous applications, making it essential to precisely predict their degradation trajectory and remaining useful life (RUL). To improve the stability and applicability of RUL prediction for lithium-ion batteries, this paper uses a new method to predict RUL by combining CNN-LSTM-Attention with transfer learning. The presented model merges the strengths of both convolutional and sequential architectures, and it enhances the model’s capability to grasp comprehensive information by utilizing the attention mechanism, thereby boosting overall performance. The CEEMDAN algorithm is used for NASA batteries with obvious capacity regeneration phenomena to alleviate the difficulties caused by capacity regeneration on model prediction. During the model transfer phase, the CNN and LSTM layers of the pre-trained model from the source domain are kept unchanged during retraining, while the attention and fully connected layers are fine-tuned for NASA batteries and self-tested NCM batteries. The final results indicate that this method achieves superior accuracy relative to other methods while addressing the issue of limited labeled data in the target domain through transfer learning, thereby enhancing the model’s transferability and generalization capabilities.

The electrochemical impedance spectrum (EIS) is a non-destructive technique for the on-line evaluation and monitoring of the performance of lithium-ion batteries. However, the measured EIS can be unstable and inaccurate without the proper resting time. Therefore, we conducted comprehensive EIS tests during the charging process and at different state of charge (SOC) levels with various resting times. The test results revealed two findings: (1) EIS tests with a constant long resting time showed a clear pattern in the impedance spectral radius—a decrease followed by a slight increase. We analyzed the impedance data using an equivalent circuit model and explained the changes through circuit parameters. (2) We examined the effect of resting time on impedance at consistent SOC levels. While low SOC levels exhibited significant sensitivity to resting time, medium SOC levels showed less sensitivity, and high SOC levels had minimal impact on resting time. The equivalent circuit parameters matched the observed trends. Kramers–Kronig transformation was conducted to assess the reliability of the experiments. This study not only summarizes the relationship between the EIS and SOC but also highlights the importance of resting time in impedance analysis. Recognizing the role of the resting time could enhance impedance-based battery studies, contribute to refined battery status evaluation, and help researchers to design proper test protocols.

The stability of current collector in high voltage LiNi0.5Mn1.5O4 batteries and the rate performance and cyclic stability of LiNi0.5Mn1.5O4 electrodes using three current collectors were investigated, including an Al foil, an etched Al foil (E-Al), and a carbon-coated Al foil (C-Al). The oxidation stability of current collector at high voltage is significantly improved by carbon coating on the Al foil, which can make a good contact between the current collector and active layer. The surface structure and composition of the current collectors strongly affect the rate capability and cyclic performance of LiNi0.5Mn1.5O4 electrodes. Interestedly, using the C-Al current collector not only remarkably increases the rate capability with the capacity of 78 mAh g-1 and the average discharge voltage of 4.1 V at 40 C-rate, but also greatly enhances its cyclic stability at 55°C. Furthermore, the formation of a thin and uniform surface film is observed on the cycled LiNi0.5Mn1.5O4 electrode using the C-Al current collector, which can be attributed to reducing the electrode polarization and suppressing the electrolyte decomposition, leading to good capacity retention. In brief, the carbon-coated Al foil can be applicable to the high voltage LiNi0.5Mn1.5O4 material of Li-ion battery. [ABSTRACT FROM AUTHOR]

Battery degradation will happen in the process of storage, resulting in capacity diminishment and augmentation of inherent resistance. The understanding of the aging mechanism is crucial to predict the state-of-health of lithium-ion batteries (LIBs). In this paper, a pseudo-OCV model of a LIBs is developed to investigate the evolution of internal parameters, and a degradation model which can be used for predicting the calendar life of the battery is developed. The good agreement between the calculated and experimental results is the evidence that the models are able to accurately capture the degradation process of the battery. In addition, we use both incremental capacity and differential voltage analysis to research the aging mechanism of a lithium ion battery. Our results revealed that the capacity fading during the aging process is predominantly caused by the loss of Li-ions and self-discharge. [ABSTRACT FROM AUTHOR]

A physics-based model of a lithium-ion battery with a blended electrode is developed on the basis of a pseudo-two-dimensional (P2D) model. Through a simplification of the physics-based model, an equilibrium model is developed that can be used for a qualitative and quantitative analysis of the blended electrode. The good agreement between the calculated and experimental results is the evidence that the equilibrium model is able to accurately capture the characteristics of the blended electrode. In addition, we use both the physics-based model and the equilibrium model to analyze the storage and cycling performance and the aging mechanism of a lithium ion battery containing a blended electrode. [ABSTRACT FROM AUTHOR]