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基于NGO-GRU的锂电池健康状态估计研究

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为提高锂离子电池健康状态(SoH)估计的精度和稳定性,提出了一种基于北方苍鹰优化(NGO)算法优化门控循环单元(GRU)网络的SoH估计模型.从电池充放电电压、温度的历史数据中提取多个健康因子作为模型的输入数据,利用NGO智能寻优GRU网络的隐藏单元数目、学习率和最大训练周期数等超参数;通过优化后的NGO-GRU估计模型构建锂电池健康因子与SoH的映射关系,实现SoH的快速估计;使用NASA数据集验证算法的有效性.对比其他几种模型,结果表明,NGO-GRU估计模型具有更高的估计精度和稳定性.
Research on SoH Estimation of Lithium Batteries Based on NGO-GRU
To improve the accuracy and stability of state of health(SoH)estimation for lithium-ion batteries,a SoH estimation model based on the Northern Goshawk Optimization(NGO)algorithm for optimizing the Gated Recurrent Unit(GRU)network was proposed.Multiple health factors from the historical data of battery charging and discharging voltage and temperature are extracted as input data for the model,and NGO intelligent optimization of hyperparameters such as the number of hidden units,learning rate,and maximum training period of the GRU network are used.A mapping rela-tionship between lithium battery health factors and SoH is constructed through the optimized NGO GRU estimation model to achieve rapid estimation of SoH.NASA datasets are used to verify the effectiveness of the algorithm.Compared with other models,the results show that the NGO-GRU estimation model has higher estimation accuracy and stability.

lithium batterySoH estimationGRU networkNGO algorithm

朱成杰、余梦书、潘子良

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安徽理工大学 电气与信息工程学院,安徽淮南 232001

锂电池 健康状态估计 门控循环单元网络 北方苍鹰优化算法

国家自然科学基金

62003001

2024

仪表技术
上海市仪器仪表学会,上海仪器仪表研究所等

仪表技术

影响因子:0.217
ISSN:1006-2394
年,卷(期):2024.(2)
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