基于PCA和GA-BP神经网络的锂电池容量估算方法
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河南科技大学车辆与交通工程学院 河南 洛阳 471003

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TM912

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国家重点研发计划项目(双电机独立驱动电动拖拉机能量管理关键技术研究与整机控制器开发,2016YFD0701002);河南省科技攻关项目(基于框架体系的拖拉机虚拟试验平台技术开发及应用,212102210328)


Lithium battery capacity estimation method based on PCA and GA-BP neural network
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School of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang, Henan 471003

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    摘要:

    本文针对车用锂离子动力电池容量估算方法精度不高的问题,提出了一种利用遗传算法优化BP神经网络的锂离子电池剩余容量估算方法。首先在整理NASA锂离子电池数据集后,得到不同健康状态下电池的容量增量曲线峰值。其次将健康因子进行主成分分析对其降维处理,利用遗传算法优化BP神经网络的连接权值,对锂离子电池容量进行预测。最后在NASA不同型号的电池上应用模型进行了验证。结果表明,所提出的方法可以在不同训练量的情况下准确估算4种锂离子电池的容量,其估算的方均根误差小于2%,且与未使用遗传算法优化的预测结果相比,该方法具有较高的预测精度。

    Abstract:

    Aiming at the problem that the capacity estimation method of lithium-ion battery for vehicle is not high precision, a method of residual capacity estimation of lithium-ion battery based on BP neural network optimized by genetic algorithm is proposed in this paper. First, after collating NASA's lithium-ion battery data set, the peak value of battery capacity increment curve under different health conditions was obtained. Secondly, the health factor was analyzed by principal component analysis to reduce its dimension, and the connection weight of BP neural network was optimized by genetic algorithm to predict the capacity of lithium ion battery. Finally, the model was validated on different NASA batteries. The results show that the proposed method can accurately estimate the capacity of four kinds of lithium ion batteries under different training amounts, and the square mean error of the estimation is less than 2%, and the proposed method has higher prediction accuracy than the prediction results without genetic algorithm optimization.

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吴 琼,徐锐良,杨晴霞,徐立友.基于PCA和GA-BP神经网络的锂电池容量估算方法[J].电子测量技术,2022,45(6):66-71

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  • 在线发布日期: 2024-05-16
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