基于混合核极限学习机的锂电池状态融合估计-电气工程与自动化-CSCIED科技核心评价数据库-手机版

基于混合核极限学习机的锂电池状态融合估计

State of charge estimation of lithium-ion batteries incorporating a hybrid kernel extreme learning machine

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DOI	10.12208/j.jeea.20250195
刊名	电气工程与自动化 Journal of Electrical Engineering and Automation
年，卷(期)	2025, 4(5)
作者	程艳云, 丁洁, 沈德智, 王赛,
作者单位	南京邮电大学自动化学院江苏南京
摘要	在智能交通系统中，电动汽车的能源管理效率直接影响交通系统的可靠性与可持续性，而锂离子电池荷电状态（State of Charge, SOC）的精准估计是其重中之重。针对SOC的非线性和动态特征，本文提出了一种基于多源数据融合的电池荷电状态估计模型。在数据预处理阶段，首先利用自适应噪声完全集合经验模态分解和小波阈值降噪技术对输入的电压和电流信号进行分解和去噪处理，便于后续SOC估计；在特征提取阶段，采用卷积神经网络对去噪后的电压和电流数据进行特征提取，并将提取的电压和电流信号中的隐含特征与去噪后的电压电流数据一起作为混合核极限学习机的输入，利用其非线性拟合能力和泛化能力对SOC进行估计；最后将混合核极限学习机的估计值作为卡尔曼滤波器的测量值，对SOC进行实时估计，提高SOC估计的准确性以及模型的鲁棒性。基于McMaster大学提供的实验数据，对比其他模型，所提出的模型在SOC估计精度上表现出显著优势，具体而言，在25°C时均方根误差从1.95%降低到0.31%，平均绝对误差从2.11%降低到0.22%。
Abstract	In intelligent transportation systems, the energy management efficiency of electric vehicles directly affects the reliability and sustainability of the transportation system. Accurately estimating the State of Charge (SOC) of lithium-ion batteries is of paramount importance. Considering the nonlinear and dynamic characteristics of SOC, this paper proposes a battery SOC estimation model based on multi-source data fusion. During the data preprocessing stage, the input voltage and current signals are decomposed and denoised by complete ensemble empirical modal decomposition with adaptive noise and wavelet threshold denoising techniques to facilitate subsequent SOC estimation. In the feature extraction stage, a convolutional neural network is employed to extract features from the denoised voltage and current data. The extracted implicit features of the voltage and current signals, along with the denoised voltage and current data, are utilized as inputs to the hybrid kernel extreme learning machine, which leverages its nonlinear fitting and generalization capabilities to estimate the SOC. Finally, the estimated value from the hybrid kernel extreme learning machine is utilized as the measurement value for the kalman filter to perform real-time SOC estimation, enhancing the accuracy of SOC estimation and the robustness of the model. Based on experimental data provided by McMaster University, compared with other models, the proposed model demonstrates a significant advantage in SOC estimation accuracy. Specifically, the root mean square error is reduced from 1.95% to 0.31%, and the mean absolute error is reduced from 2.11% to 0.22% at 25°C.
关键词	荷电状态估计；混合核极限学习机；卷积神经网络；自适应噪声完全集合经验模态分解；小波阈值降噪；卡尔曼滤波
KeyWord	State of charge; HKELM; CNN; Complete ensemble empirical mode decomposition with adaptive noise; Wavelet threshold denoising; Kalman filter
基金项目
页码	132-140

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引用本文

程艳云, 丁洁, 沈德智, 王赛. 基于混合核极限学习机的锂电池状态融合估计 [J]. 电气工程与自动化. 2025; 4; (5). 132 - 140.

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