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Bibliographic Details
Main Authors:	Rafiqul Islam Roky, Shahin Shaikh, Md Nazmulhuda
Format:	Recurso digital
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Published:	Zenodo 2025
Subjects:	UKF-RBF
Online Access:	https://doi.org/10.5281/zenodo.14623346
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This paper introduces an enhanced Unscented Kalman Filter (UKF) algorithm integrated with Radial Basis Function (RBF) neural networks to advance the accuracy of nonlinear state estimation in dynamic systems. Our approach specifically addresses the estimation of the State of Charge (SOC) of a battery, leveraging a second-order equivalent circuit model to capture the battery’s complex behavior. The innovation of our method lies in the integration of RBF neural networks into the UKF framework, which enhances the algorithm’s capability to model nonlinearities and improve prediction accuracy.The standard UKF algorithm, while robust in handling nonlinear systems, often struggles with certain nonlinearities inherent in battery SOC estimation. By incorporating an RBF neural network, which excels at approximating complex, nonlinear relationships, our proposed UKF-RBF algorithm achieves superior performance. The RBF network is trained to capture the nonlinear Open Circuit Voltage (OCV) vs. SOC relationship, which is crucial for accurate SOC estimation.Experimental results demonstrate that the UKF-RBF algorithm significantly outperforms the traditional UKF in terms of Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The UKF-RBF algorithm shows marked improvements in SOC estimation accuracy across varying operating conditions and temperatures, making it a robust solution for practical applications in battery management systems. The integration of RBF neural networks into the UKF framework represents a novel approach that bridges the gap between traditional Kalman filtering and modern neural network techniques, providing a substantial enhancement in the estimation of nonlinear states.

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