In order to solve the imminent problem in that the traditional protection strategy cannot meet time requirements, together with the fact that the rotational inertia of a DC microgrid is small and short-circuit fault develops rapidly, a bidirectional short-circuit current blocker (BSCCB) based on solid-state circuit breaker for a DC microgrid is proposed. Firstly, the bidirectional current blocking circuit structure is proposed based on the analysis of key components. Then, a top-level differential protection strategy is developed based on the aforementioned proposal. Finally, the performance of the blocking circuit is simulated and verified by experiments. The results show that the proposed method can block short-circuit current within 4 ms, and the response speed of the protection strategy is very fast compared with previous approaches. BSCCB also has reclosing, bidirectional blocking and energy releasing functions. The current blocker proposed in this paper can be reused multiple times and has a promising future in low-voltage DC microgrid application.
State-of-Charge (SOC) estimation of lithium-ion batteries have a great significance for ensuring the safety and reliability of battery management systems in electrical vehicle. Deep learning method can hierarchically extract complex feature information from input data by building deep neural networks (DNNs) with multi-layer nonlinear transformations. With the development of graphic processing unit, the training speed of the network is faster than before, and it has been proved to be an effective data-driven method to estimate SOC. In order to further explore the potential of DNNs in SOC estimation, take battery measurements like voltage, current and temperature directly as input and SOC as output, an improved method using the Nesterov Accelerated Gradient (NAG) algorithm based Bidirectional Gated Recurrent Unit (Bi-GRU) network is put forward in this paper. Notably, to address the oscillation problem existing in the traditional gradient descent algorithm, NAG is used to optimize the Bi-GRU. The gradient update direction is corrected by considering the gradient influence of the historical and the current moment, combined with the estimated location of the parameters at the next moment. Compared to state-of-the-art estimation methods, the proposed method enables to capture battery temporal information in both forward and backward directions and get independent context information. Finally, two well-recognized lithium-ion batteries datasets from University of Maryland and McMaster University are applied to verify the validity of the research. Compared with the previous methods, the experimental results demonstrate that the proposed NAG based Bi-GRU method for SOC estimation can improve the precision of the prediction at various ambient temperature.INDEX TERMS State-of-Charge estimation, lithium-ion batteries, Bidirectional Gated Recurrent Unit, Nesterov Accelerated Gradient.
Active battery equalization and passive battery equalization are two important methods which can solve the inconsistency of battery cells in lithium battery groups. In this paper, a new hybrid battery equalization strategy combinfigureing the active equalizing method with a passive equalizing method is proposed. Among them, the implementation of the active equalizing method uses the bidirectional Flyback converter and Forward converter. This hybrid equalizing strategy adopts the concept of hierarchical equilibrium: it can be divided into two layers, the top layer is the equalization between groups, and the bottom layer is the equalization of group. There are three active equilibrium strategies and one passive equilibrium strategy. For verification purposes, a series of experiments were conducted in MATLAB 2018b/Simulink platform. The simulation and experiment results show that this hybrid battery equalizing method is efficient and feasible.
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