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  • Open Access

    ARTICLE

    A Power Battery Fault Diagnosis Method Based on Long-Short Term Memory-Back Propagation

    Yuheng Yin, Jiahao Song*, Minghui Yang

    Energy Engineering, Vol.122, No.2, pp. 709-731, 2025, DOI:10.32604/ee.2024.059021 - 31 January 2025

    Abstract The lithium battery is an essential component of electric cars; prompt and accurate problem detection is vital in guaranteeing electric cars’ safe and dependable functioning and addressing the limitations of Back Propagation (BP) neural networks in terms of vanishing gradients and inability to effectively capture dependencies in time series, and the limitations of Long-Short Term Memory (LSTM) neural network models in terms of risk of overfitting. A method based on LSTM-BP is put forward for power battery fault diagnosis to improve the accuracy of lithium battery fault diagnosis. First, a lithium battery model is constructed… More > Graphic Abstract

    A Power Battery Fault Diagnosis Method Based on Long-Short Term Memory-Back Propagation

  • Open Access

    ARTICLE

    Kalman Filter Estimation of Lithium Battery SOC Based on Model Capacity Updating

    Min Deng1, Quan Min1, Ge Yang1, Man Yu2,3,*

    Energy Engineering, Vol.119, No.2, pp. 739-754, 2022, DOI:10.32604/ee.2022.018025 - 24 January 2022

    Abstract High-precision estimation of lithium battery SOC can effectively optimize vehicle energy management, improve lithium battery safety protection, extend lithium battery cycle life, and reduce new energy vehicle costs. Based on the forgetting factor recursive least square method (FFRLS), Thevenin equivalent circuit model and Singular Value Decomposition-Unscented Kalman Filter (SVD-UKF), the SVD-UKF combined lithium battery SOC estimation algorithm with model capacity update is proposed, aiming at further improving the SOC estimation accuracy of lithium battery. The parameter identification of Thevenin model is studied by using the forgetting factor recursive least square method. To overcoming the shortcomings… More >

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