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The aim is to enhance the accuracy and robustness of state of charge estimation for lithium-ion batteries under non-Gaussian noise.

January 17, 2026BatteriesOpen Access

State-of-Charge Estimation of Lithium-Ion Batteries Based on GMMCC-AEKF in Non-Gaussian Noise Environment

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The aim is to enhance the accuracy and robustness of state of charge estimation for lithium-ion batteries under non-Gaussian noise.
Developed the GMMCC-AEKF algorithm using a generalized Gaussian kernel approach.
Implemented adaptive filtering techniques to adjust covariance matrices dynamically.
Conducted experiments with lithium-ion cells at different temperatures and test profiles.
GMMCC-AEKF improved estimation accuracy by 86.54% over EKF under specific conditions.
Achieved an accuracy boost of 55.89% over GMMCC-EKF in another test.
Maintained high accuracy and stable performance even at extreme temperatures (10 °C, 40 °C).

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This algorithm improves state of charge estimation in lithium-ion batteries, indicating higher accuracy under various noise conditions.

Li et al. (2026) studied this question.

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