基于改进白鲸算法优化BiTCN-BiGRU的锂电池SOC估计
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1.磁浮技术与磁浮列车教育部重点实验室 成都 610031;2.西南交通大学电气工程学院 成都 611756

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TM912;TN98

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SOC estimation of lithium batteries based on BiTCN-BiGRU optimized by improved beluga whale algorithm
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1.Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education,Chengdu 610031, China; 2.School of Electrical Engineering, Southwest Jiaotong University,Chengdu 611756, China

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    摘要:

    电池荷电状态(SOC)是电动汽车锂电池管理的核心参数之一,本文提出一种基于改进白鲸算法优化BiTCN-BiGRU的锂电池SOC估计模型。首先搭建双向时域卷积网络(BiTCN)和双向门循环单元(BiGRU)组合的SOC估计模型,然后使用白鲸算法(BWO)对BiTCN-BiGRU模型超参数寻优以充分发挥组合网络模型的优势,并且分别在传统BWO的探索阶段和鲸落阶段引入改进策略以解决传统BWO容易陷入局部最优且收敛速度慢的问题。最后基于开源锂电池充放电数据集验证改进后SOC估计模型的性能,结果表明在3种温度的标准化城市循环工况下,改进白鲸算法优化BiTCN-BiGRU模型的SOC估计平均绝对误差为0.428%,均方根误差为0.38%,能很好的应用于锂电池SOC估计。

    Abstract:

    The state of charge (SOC) of the battery is one of the core parameters for managing lithium batteries in electric vehicles. This paper proposes a lithium battery SOC estimation model based on an improved white whale algorithm optimized BiTCN-BiGRU. Firstly, a SOC estimation model combining bidirectional time domain convolutional network (BiTCN) and bidirectional gated recurrent unit (BiGRU) is constructed. Then, the beluga whale optimization (BWO) is used to optimize the hyperparameters of the BiTCN-BiGRU model to fully leverage the advantages of the combined network model. Improvement strategies are introduced in the exploration and whale fall stages of traditional BWO to solve the problem of traditional BWO easily falling into local optima and slow convergence speed. Finally, the performance of the improved SOC estimation model was verified based on the open-source lithium battery charging and discharging dataset. The results showed that under standardized urban cycling conditions at three temperatures, the improved white whale algorithm optimized the BiTCN-BiGRU model SOC estimation with an average absolute error of 0.428% and a root mean square error of 0.38%, which can be well applied to lithium battery SOC estimation.

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柳博,吴松荣,付聪,王少惟,张驰.基于改进白鲸算法优化BiTCN-BiGRU的锂电池SOC估计[J].电子测量技术,2025,48(9):75-83

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  • 在线发布日期: 2025-05-23
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