A lithium battery state-of-charge assessment method based on LightGBM optimized square root central difference Kalman filter.

By combining LightGBM and square root central difference Kalman filter algorithms, the problem of insufficient accuracy and stability in the estimation of the state of charge of power batteries in existing technologies is solved. In particular, in the low SOC region, higher estimation accuracy and battery utilization are achieved, thereby improving the driving range of electric vehicles.

CN116338466BActive Publication Date: 2026-05-26GUILIN UNIV OF ELECTRONIC TECH
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Patent Information

Application Number
CN202310173499.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-02-28
Publication Date
2026-05-26
Estimated Expiration
2043-02-28

AI Technical Summary

Technical Problem

Existing power battery state-of-charge estimation methods, such as EKF and CDKF, have shortcomings in accuracy and stability. In particular, they are difficult to achieve accurate state estimation in the low SOC region, resulting in ineffective utilization of battery capacity, affecting the driving range of electric vehicles and causing energy waste.

Method used

We employ a LightGBM-based optimized square root central difference Kalman filter algorithm, combined with the forgetting factor recursive least squares method and a second-order equivalent circuit model. By using the LightGBM algorithm to predict innovations in the low SOC region and incorporating them into the SRCCDKF, we improve the estimation accuracy.

Benefits of technology

Higher accuracy and stability were achieved in the low SOC region, significantly improving the estimation of state of charge and enhancing battery utilization and range.

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Abstract

This invention discloses a lithium battery state-of-charge (SOC) assessment method based on LightGBM optimized square root central difference Kalman filter, comprising the following steps: (A) establishing a second-order equivalent circuit model of the lithium battery; (B) identifying parameters of the second-order equivalent circuit model of the lithium battery using the forgetting factor recursive least squares method; (C) estimating SOC using square root central difference Kalman filter (SRCDKF); (D) using the historical information of SCDKF as training data for the LightGBM algorithm in the low SOC region, then using the LightGBM algorithm to predict new information, and merging the new information into the SCDKF. Compared with the prior art, this invention introduces the LightGBM algorithm into SCDKF to further improve the prediction accuracy, resulting in a SOC assessment method with good convergence even in the low SOC region.
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