Closed-loop state of charge estimation of Li-ion batteries based on deep learning and robust adaptive Kalman filter

Qi, Wei; Qin, Wenhu; Yun, Zhonghua

doi:10.1016/j.energy.2024.132805

Energy

2024

DOI: 10.1016/j.energy.2024.132805

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Closed-loop state of charge estimation of Li-ion batteries based on deep learning and robust adaptive Kalman filter

Wei Qi,

Wenhu Qin,

Zhonghua Yun

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Cited by 2 publications

References 45 publications

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Prediction model for aero-engine combustor temperature field with physical constraints of high temperature deviation

Wang,

Kong,

Han

et al. 2024

Energy

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Prediction model for aero-engine combustor temperature field with physical constraints of high temperature deviation

Wang,

Kong,

Han

et al. 2024

Energy

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Mechanism and Data-Driven Fusion SOC Estimation

Tian,

Xue,

Zhou

et al. 2024

Energies

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An accurate assessment of the state of charge (SOC) of electric vehicle batteries is critical for implementing frequency regulation and peak shaving. This study proposes mechanism- and data-driven SOC fusion calculation methods. First, a second-order Thevenin battery model is developed to obtain the physical parameters of the battery. Second, data from the Thevenin battery model and data from four standard cycling conditions in the electric vehicle industry are added to the dataset of the feed-forward neural network data-driven model to construct the test and training sets of the data-driven model. Finally, the error of the mechanism and data-driven fusion modeling method is quantitatively analyzed by comparing the estimation error of the method for the battery SOC at different temperatures with the accuracy of the data-driven SOC estimation method. The simulation results show that the root mean square error, the mean age absolute error, and the maximum error of mechanism and data-driven method for the estimation error of battery SOC are lower than those of the data-driven method by 0.9%, 0.65%, and 1.3%, respectively. The results show that the mechanism and data-driven fusion SOC estimation method has better generalization performance and higher SOC estimation accuracy.

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Closed-loop state of charge estimation of Li-ion batteries based on deep learning and robust adaptive Kalman filter

Cited by 2 publications

References 45 publications

Prediction model for aero-engine combustor temperature field with physical constraints of high temperature deviation

Prediction model for aero-engine combustor temperature field with physical constraints of high temperature deviation

Mechanism and Data-Driven Fusion SOC Estimation

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