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- W3194830109 abstract "State of health (SoH) prediction of Lithium-ion batteries is very essential to monitor and optimize battery behavior and safety in the battery management system (BMS). However, the capacity degradation of the battery is subject to nonlinear phenomena influenced by temperature variation, which is one of the main difficulties in SoH prediction. To solve this difficulty and improve the prediction accuracy, this paper proposes a novel multi-scale model for SoH prediction. Firstly, the temperature characteristic data is dealt with in the frequency domain to overcome the nonlinear fluctuations of the battery capacity degradation. The alternative frequency bands for the temperature characteristics and the capacity degradation of battery are determined by means of wavelet packet transform (WPT) and correlation analysis. Then, a prediction framework based on wavelet neural network and ensemble learning is developed, in which expectation maximization (EM) algorithm is used to optimize the prediction model. Finally, The prediction accuracy and robustness are verified by two data sets of NASA lithium-ion battery. Compared with other data-driven methods, the results show that the root-mean-square error (RMSE) value of the proposed method is less than 1.33% in the SoH prediction." @default.
- W3194830109 created "2021-08-30" @default.
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- W3194830109 date "2021-10-01" @default.
- W3194830109 modified "2023-10-02" @default.
- W3194830109 title "A multi-scale state of health prediction framework of lithium-ion batteries considering the temperature variation during battery discharge" @default.
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- W3194830109 doi "https://doi.org/10.1016/j.est.2021.103076" @default.
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