Lithium battery SOC intelligent estimation method, electronic equipment and medium

CN120802064AActive Publication Date: 2025-10-17NORTH CHINA ELECTRIC POWER UNIV
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Patent Information

Application Number
CN202511309426.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-15
Publication Date
2025-10-17
Estimated Expiration
2045-09-15

AI Technical Summary

Technical Problem

Accurate estimation of lithium battery SOC faces significant challenges under temperature fluctuations, aging attenuation and dynamic operating conditions. Existing physical models lack nonlinear processing capabilities, and data-driven models are not very interpretable, resulting in insufficient estimation accuracy and reliability.

Method used

The first-order equivalent circuit model is combined with the characteristic time multilayer perceptron, and the SOC estimation is performed through the adaptive unscented Kalman filter algorithm. The terminal voltage error is dynamically compensated by the fusion voltage, and the target state space model is constructed to achieve dynamic tracking of the lithium battery operating status.

Benefits of technology

The accuracy and robustness of lithium battery SOC estimation are improved, the model complexity is reduced, and high-precision tracking and management of lithium battery status are achieved.

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Abstract

The invention provides a lithium battery SOC intelligent estimation method, electronic equipment and a medium. The method comprises the following steps: establishing a first-order equivalent circuit model of the lithium battery; based on the first-order equivalent circuit model fitting terminal voltage, obtaining a first fitting terminal voltage; establishing a terminal voltage prediction model of the lithium battery based on the characteristic time multi-layer perceptron, and fitting the terminal voltage to obtain a second fitting terminal voltage; according to the voltage difference between the first fitting end voltage and the actual end voltage and the voltage difference between the second fitting end voltage and the actual end voltage, the two fitting end voltages are fused, and fused voltage is obtained; constructing a target state space model based on the first-order equivalent circuit model and the fusion voltage; and inputting the real-time terminal voltage, the real-time current and the real-time temperature of the lithium battery into the target state space model, and performing state estimation on the target state space model by using an adaptive unscented Kalman filtering algorithm to obtain an SOC estimation value. According to the scheme, the accurate estimation of the SOC can be realized.
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Citation Information

Patent Citations

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