基于分数阶模型的储能用锂离子电池荷电状态估计
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TK02;TN40

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国家自然科学基金(62203277)、山东省自然科学基金(ZR2021QD066) 项目资助


State of charge estimation for lithium-ion batteries for energy storage based on the fractional unscented Kalman filter
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    摘要:

    锂电池荷电状态(state of charge,SOC)的准确估计对于新型储能系统的高效运行至关重要,为提升锂电池SOC 估计的 精度,提出了一种基于分数阶无迹卡尔曼滤波(fractional order unscented Kalman filter,FOUKF)算法和带自适应遗忘因子的递推 最小二乘法(recursive least square method with adaptive forgetting factor,AFFRLS)来估计锂电池的 SOC。首先,提出了基于分数 阶微积分理论的二阶RC 模型来对锂电池特性进行建模。然后进行脉冲表征测试,获得电池的端电压,并基于AFFRLS的方法 完成参数辨识。此外,所提出的基于FOUKF的算法应用于电池放电实验中进行 SOC 估计。最后,从最大绝对误差(MAE)、平 均绝对误差(AAE)和均方根误差(RMSE)3项预测指标与对比方法进行比较。实验结果表明,FOUKF算法对SOC的估计MAE 小于2% ,AAE以及RMSE均小于0.8%,实验结果表明所提算法具有较高的精度和抗干扰能力。

    Abstract:

    Efficient operation of new energy storage systems relies heavily on accurately estimating the state of charge (SOC)of lithiumion batteries.In order to improve the accuracy of estimating SOC of lithium batteries,a method based on fractional order unscented Kalman filter(FOUKF)algorithm and recursive least square method with adaptive forgetting factor(AFFRLS)is proposed to estimate SOC of lithium battery.Firstly,a second-order RC model based on fractional-order calculus theory was developed to model the lithium battery characteristics.Then perform a pulse characterization test to obtain the battery terminal voltage,and complete parameter identification based on AFFRLS.In addition,the proposed algorithm based on FOUKF is applied to estimateSOC in battery discharge experiments.Finally, comparedthe three prediction indicators of maximum absolute error(MAE),average absolute error(AAE)and root mean square error(RMSE)with the comparison method.The experimental results show that the estimated MAE of sOC by FOUKF algorithm is less than2%,and the AAEand RMSE are both lessthan0.8%.The experimental results show that the proposed algorithm has high accuracy and anti-interference ability.

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马 昕,丁兴科,田崇翼,田长彬,孔维政,冯媛媛,刘 强,闫 安.基于分数阶模型的储能用锂离子电池荷电状态估计[J].国外电子测量技术,2024,43(8):141-149

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  • 在线发布日期: 2024-10-11
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