State-of-Charge Estimation of Lithium-Ion Battery Based on Convolutional Neural Network Combined with Unscented Kalman Filter

Batteries Pub Date : 2024-06-04 DOI:10.3390/batteries10060198

Hongli Ma, Xinyuan Bao, António Lopes, Liping Chen, Guoquan Liu, Min Zhu

引用次数: 0

Abstract

Estimation of the state-of-charge (SOC) of lithium-ion batteries (LIBs) is fundamental to assure the normal operation of both the battery and battery-powered equipment. This paper derives a new SOC estimation method (CNN-UKF) that combines a convolutional neural network (CNN) and an unscented Kalman filter (UKF). The measured voltage, current and temperature of the LIB are the input of the CNN. The output of the hidden layer feeds the linear layer, whose output corresponds to an initial network-based SOC estimation. The output of the CNN is then used as the input of a UKF, which, using self-correction, yields high-precision SOC estimation results. This method does not require tuning of network hyperparameters, reducing the dependence of the network on hyperparameter adjustment and improving the efficiency of the network. The experimental results show that this method has higher accuracy and robustness compared to SOC estimation methods based on CNN and other advanced methods found in the literature.

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基于卷积神经网络和无符号卡尔曼滤波器的锂离子电池充电状态估计

估算锂离子电池（LIB）的充电状态（SOC）是确保电池和电池供电设备正常运行的基础。本文提出了一种新的 SOC 估算方法（CNN-UKF），该方法结合了卷积神经网络（CNN）和无香卡尔曼滤波器（UKF）。LIB 的测量电压、电流和温度是 CNN 的输入。隐藏层的输出为线性层提供输入，线性层的输出对应于基于网络的初始 SOC 估算。然后，CNN 的输出被用作 UKF 的输入，UKF 利用自校正功能获得高精度 SOC 估算结果。这种方法无需调整网络超参数，减少了网络对超参数调整的依赖，提高了网络的效率。实验结果表明，与基于 CNN 的 SOC 估算方法和文献中的其他先进方法相比，该方法具有更高的准确性和鲁棒性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Batteries

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