At present, the rapid development of new energy sources makes lithium-ion batteries (LIBs) widely used, but LIBs will inevitably age during using. State of health (SOH) is a direct reflection to the aging of LIBs, so it is necessary to estimate the SOH. During the aging process of the LIBs, the phenomenon of capacity recovery will occur if the battery is standing for too long. Existing SOH estimation methods based on neural network do not propose countermeasures for the phenomenon, but in fact, capacity recovery is inevitable and it has a great impact on SOH estimation. According to this vacancy, this paper proposes a SOH estimation method based on double bi-directional long shortterm memory (DBiLSTM) model, which can accurately estimate recovered capacity and improve accuracy of SOH estimation. First, the capacity of LIB is decomposed at multiple scales using wavelet analysis, and the smooth and fluctuating components are obtained. Then six features are proposed based on the changes in the battery after aging. The proposed features are decomposed into new features suitable for the two components. Finally, the smooth component and the fluctuation component are estimated synchronously, and the estimated results are reconstructed to obtain the final estimated SOH. The method proposed in this paper is verified in the NASA dataset and compared with the bi-directional long short-term memory (BiLSTM) model. Comparing with the direct estimation by BiLSTM, the root mean square error (RMSE) is reduced by at least 0.0084 and the mean absolute percentage error (MAPE) is reduced by at least 0.52% when the battery capacity fluctuates greatly. The experimental results show that the proposed method can significantly improve the accuracy of SOH estimation when the capacity fluctuates greatly.Abbreviations: BiLSTM, bi-directional long short-term memory; CC, constant current; CNN, convolution neural network; CV, constant voltage; DBiLSTM, double bi-directional long short-term memory; FN, Nth feature; FN HF , the high frequency components of the Nth feature; FN LF , the low frequency component of the Nth feature; LIB, lithium-ion battery; LSTM, long short-term memory; MAPE, mean absolute percentage error; MLP, multi-layer perceptron; PCoE, prognostics center of excellence; RMSE, root mean square error; SOH, state of health; SVR, support vector regression.
To solve the insufficient frequency regulation capacity and inertia of the power system caused by the increase of grid-connected wind capacity, a combined wind-storage frequency regulation control strategy considering the optimized intervals of the energy storage system is proposed. This article selects the joint frequency regulation of wind turbine overspeed control and energy storage virtual synchronous control, and virtual synchronous control is used as the upper-level control of the entire wind-storage system. In the process of frequency modulation, the output power of wind turbine and energy storage is reasonably distributed through the upper control. On this basis, to ensure the long-term and stable operation of energy storage, the state of charge (SOC) partition principle of energy storage is set up, which is divided into different areas according to the size of the SOC, and the output of the energy storage device is adjusted according to the divided regions. The results of simulation analysis and verification show that the proposed strategy provides additional frequency regulation capacity and inertia during the frequency variation and greatly improves the frequency stability. Besides, the SOC zoning principle ensures the stable operation of the energy storage.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.