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Remaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the influence of the relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel RUL prediction method based on Wiener processes. This method can simplify the modeling complexity by using the RUT to model the recovery process. First, the life cycle of a lithium-ion battery is divided into the degradation processes that eliminate the relaxation effect and the recovery processes caused by relaxation effect. Next, the degradation model, after eliminating the relaxation effect, is established based on linear Wiener processes, and the model for RUT is established by using normal distribution. Then, the prior parameters estimation method based on maximum likelihood estimation and online updating method under the Bayesian framework are proposed. Finally, the experiments are carried out according to the degradation data of lithium-ion batteries published by NASA. The results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction and has a strong engineering application value. [ABSTRACT FROM AUTHOR]

A plasma jet-triggered gas switch (PJT-GS) has been developed as an important piece of equipment to operate in an ± 800 kV ultra-high voltage direct current transmission system (UHV DC) to achieve grid system protection and control. The crucial factors that would affect its operational performance, such as the current level the PJT-GS could withstand and the gas gap distance between the two rotating electrodes, are comparatively studied in the present work by analysing the arc dynamic characteristics. The rotating electrode used in the PJT-GS is designed with a helical-slotted structure, and the arc can be rotated circularly driven by the produced transverse magnetic field (TMF) along the electrode edge. The objective of such research is to provide a thorough study of the arc dynamic behaviour during the current flowing process of the PJT-GS and also to characterise the physical mechanism that affects the arc rotation and the PJT-GS operation performance. The magnetohydrodynamic-based (MHD) approach is applied by establishing a 3D arc model. Following such a study, the variation of arc characteristics under different operation conditions could be thoroughly determined and it also could provide the guidance for the PJT-GS optimum design reasonably to support its corresponding engineering applications. [ABSTRACT FROM AUTHOR]

This paper presents a method for use in estimating the state of charge (SOC) of lithium-ion batteries which is based on an electrochemical impedance equivalent circuit model with a controlled source. Considering that the open-circuit voltage of a battery varies with the SOC, an equivalent circuit model with a controlled source is proposed which the voltage source and current source interact with each other. On this basis, the radial basis function (RBF) neural network is adopted to estimate the uncertainty in the battery model online, and a non-linear observer based on the radial basis function of the RBF neural network is designed to estimate the SOC of batteries. It is proved that the SOC estimation error is ultimately bounded by Lyapunov stability analysis, and the error bound can be arbitrarily small. The high accuracy and validity of the non-linear observer based on the RBF neural network in SOC estimation are verified with experimental simulation results. The SOC estimation results of the extended Kalman filter (EKF) are compared with the proposed method. It improves convergence speed and accuracy. [ABSTRACT FROM AUTHOR]