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Improving State of Charge Estimation Accuracy in Sodium-Ion Batteries Using Optimized Neural Networks
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DOI：10.1002/est2.70414.png)
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Abstract
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Accurate State of Charge (SOC) estimation is vital for the safe and reliable operation of sodium-ion (Na-ion) batteries. This paper proposes an Improved Feed-Forward Neural Network (IFFNN) that incorporates physics-motivated electrochemical features, including voltage, current, auxiliary temperature, and computed internal resistance, into a deeper multi-layer architecture with dropout regularization for robust state-of-charge estimation of Na-ion batteries. The model is evaluated on driving-cycle datasets from 3.2 Ah and 10 Ah Na-ion cells across temperatures ranging from −5°C to 45°C. Experimental results demonstrate that the IFFNN consistently outperforms a FFNN, achieving up to 47.3% reduction in MAE and 35.5% reduction in RMSE, while exhibiting higher R2 values. Feature sensitivity analysis identifies internal resistance as the dominant contributor to improved accuracy. The proposed IFFNN provides a robust, computationally efficient framework for real-time SOC estimation in Battery Management Systems, effectively capturing nonlinear electrochemical dynamics such as voltage–current coupling and temperature-dependent resistance.
Keywords:
feed forward neural network
improved feed forward neural network
sodium ion battery
state of charge
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