基于栅极递归单元和无符号卡尔曼滤波的锂离子电池充电状态估算

IF 2.4 4区 化学 Q3 CHEMISTRY, PHYSICAL
Ionics Pub Date : 2024-09-10 DOI:10.1007/s11581-024-05811-y
Chuanwei Zhang, Ting Wang, Meng Wei, Lin Qiao, Gaoqi Lian
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引用次数: 0

摘要

锂离子电池准确而稳健的电荷状态(SOC)估计对于电池管理系统至关重要。在这项研究中,我们提出了一种锂离子电池的 SOC 估算方法,该方法将栅极递归单元 (GRU) 与无cented 卡尔曼滤波 (UKF) 算法集成在一起。这种集成旨在增强 SOC 估算在复杂工作条件和不同温度下的鲁棒性。GRU 神经网络用于建立离线训练模型,而 UKF 在线估算的融合则用于获得平滑的锂离子电池 SOC 估算结果。这种方法实现了闭环 SOC 估算策略。实验选择了 18650 块和 26650 块磷酸铁锂电池,在 10℃、25℃ 和 40 ℃ 的工作温度下,在不同的充电和放电条件下进行。实验验证了所提出的 GRU 和 UKF 融合方法的高准确性和鲁棒性,均方根误差(RMSE)和平均绝对误差(MAE)均保持在 1%以内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

State of charge estimation for lithium-ion batteries based on gate recurrent unit and unscented Kalman filtering

State of charge estimation for lithium-ion batteries based on gate recurrent unit and unscented Kalman filtering

Accurate and robust state of charge (SOC) estimation for lithium-ion batteries is crucial for battery management systems. In this study, we proposed an SOC estimation approach for lithium-ion batteries that integrates the gate recurrent unit (GRU) with the unscented Kalman filtering (UKF) algorithm. This integration aims to enhance the robustness of SOC estimation under complex working conditions and varying temperatures. The GRU neural network is employed to establish an offline training model, while the fusion of the UKF online estimation is utilized to obtain smooth SOC estimation results for lithium-ion batteries. This approach realized a closed-loop SOC estimation strategy. The 18,650 and 26,650 LiFePO4 batteries were selected for experiments conducted under different charging and discharging conditions at operating temperatures of 10℃, 25℃, and 40 °C. The experiment verified the high accuracy and robustness of the proposed GRU and UKF fusion approach, with both the root mean square error (RMSE) and the mean absolute error (MAE) maintained within 1%.

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来源期刊
Ionics
Ionics 化学-电化学
CiteScore
5.30
自引率
7.10%
发文量
427
审稿时长
2.2 months
期刊介绍: Ionics is publishing original results in the fields of science and technology of ionic motion. This includes theoretical, experimental and practical work on electrolytes, electrode, ionic/electronic interfaces, ionic transport aspects of corrosion, galvanic cells, e.g. for thermodynamic and kinetic studies, batteries, fuel cells, sensors and electrochromics. Fast solid ionic conductors are presently providing new opportunities in view of several advantages, in addition to conventional liquid electrolytes.
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