The time-varying, dynamic, nonlinear, and other characteristics of lithium-ion batteries, as well as the capacity regeneration phenomenon, leads to the low accuracy of the traditional deep learning models in predicting the remaining useful life of lithium-ion batteries. This paper established a sequence-to-sequence model for remaining useful life prediction by combining the variational modal decomposition with bi-directional long short-term memory and Bayesian hyperparametric optimization. First, variational modal decomposition is used for noise reduction processing to maximize the retention of the original information of capacity degradation. Second, the capacity declining trend after noise reduction is modeled and predicted by the combination of bi-directional long-short term memory and temporal attention mechanism. In addition, a Bayesian optimizer is used to adaptively adjust the hyperparameters while training the model. Finally, the model was validated on NASA and CALCE data sets, and the results show that the model can accurately predict the future trend with only the initial 12% capacity data.

The accurate state of health (SOH) estimation of lithium-ion batteries enables users to make wise replacement decision and reduce economic losses. SOH estimation accuracy is related to many factors, such as usage time, ambient temperature, charge and discharge rate, etc. Thus, proper extraction of features from the above factors becomes a great challenge. In order to extract battery’s features effectively and improve SOH estimation accuracy, this article proposes a time convolution memory neural network (TCMNN), combining convolutional neural networks (CNN) and long short-term memory (LSTM) by dropout regularization-based fully connected layer. In experiment, the terminal voltage and charging current of the battery during charging process are collected, and input and output data sets are sorted out from the experimental battery data. Due to the limited equipment in the laboratory, only one battery can be charged and discharged at a time; the amount of battery data collected is relatively small, which will affect the extraction of features during the training process. Data augmentation algorithms are applied to solve the problem. Furthermore, in order to improve the accuracy of estimation, exponential smoothing algorithm is used to optimize output data. The results show that the proposed method can well extract and learn the feature relationship of battery cycle charge and discharge process in a long time span. In addition, it has higher accuracy than that of CNN, LSTM, Backpropagation (BP) algorithm, and Grey model-based neural network. The maximum error is limited to 3.79%, and the average error is limited to 0.143%, while the input data dimension is 514.

In thermodynamics, absolute zero is the coldest temperature and will never be reached because the thermal motion of microscopic particles never ceases. However, this situation could be changed for a collection of macroscopic particles. In the present paper, an experimental study was performed to explore the possible lowest temperature in a vertically vibrated granular system. It was found that the granular “absolute zero” temperature appears when the vibrating intensity is adjusted to about 4.6 times the gravitational acceleration. At this temperature, the macroscopic particles are arranged closely and behave like a rigid body without relative motions during the vibration. Near the absolute zero, inelastic collisions and energy transfer are responsible for the variation of granular temperature with time and vibrating parameters. Interestingly, the temperature variation reveals that the vibrated macroscopic particles are neither a crystal nor an amorphous system. This study introduces the granular entropy, including entropy generation and entropy flow, to describe the order of the vibrated particles. The entropy change could be illustrated by the temperature profiles of the granular system and its outside. It was also found that, unlike microscopic particles, which could maintain their entropy at a constant temperature, a granular system necessarily behaves completely disordered unless the system achieves the absolute zero granular temperature.