The degradation of lithium-ion battery often leads to electrical system failure. Battery remaining useful life (RUL) prediction can effectively prevent this failure. Battery capacity is usually utilized as health indicator (HI) for RUL prediction. However, battery capacity is often estimated on-line and it is difficult to be obtained by monitoring on-line parameters. Therefore, there is a great need to find a simple and on-line prediction method to solve this issue. In this paper, as a novel HI, permutation entropy (PE) is extracted from the discharge voltage curve for analyzing battery degradation. Then the similarity between PE and battery capacity are judged by Pearson and Spearman correlation analyses. Experiment results illustrate the effectiveness and excellent similar performance of the novel HI for battery fading indication. Furthermore, we propose a hybrid approach combining Variational mode decomposition (VMD) denoising technique, autoregressive integrated moving average (ARIMA), and GM(1,1) models for RUL prediction. Experiment results illustrate the accuracy of the proposed approach for lithium-ion battery on-line RUL prediction.

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With the increasing integration of distributed gen eration, distribution grids are evolving from passive networks to active grids. The existence of DGs and energy storage devices makes the transient simulation of active distribution grids more meaningful compared with that of traditional distribution grids, but at the same time limits the simulation speed and system scale. This paper presents a passivity-guaranteed model order reduction method based on Krylov subspace theory for active distribution grids. Then simulations are performed using the IEEE 123-node test feeder, proving that the proposed method is feasible as a powerful tool in typical applications of the transient simulation of active distribution grids.

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