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夏永凯, 夏向阳, 鄢笠, 等. 考虑多时间尺度特性的锂离子电池建模与参数辨识[J]. 太阳能学报, 2025,46(9):263-272.
夏永凯, 夏向阳, 鄢笠, 等. 考虑多时间尺度特性的锂离子电池建模与参数辨识[J]. 太阳能学报, 2025,46(9):263-272. DOI: doi:10.19912/j.0254-0096.tynxb.2024-0718.
针对锂离子电池采用传统参数辨识方法会导致辨识结果精度低、工况适应性差等问题
根据锂离子电池在动力学特性上表现出的不同时间尺度特性对二阶RC等效电路模型进行重构
并将其分解为快动态(FD)部分与慢动态(SD)部分
采用改进的遗忘因子递推最小二乘方法(IFRLS)和自适应卡尔曼滤波算法(AKF)对模型的FD与SD部分进行区分辨识
避免模型参数之间的相互干扰
最后在多种工况下将所提方法与传统的参数辨识方法进行对比分析
结果证明所提方法的有效性与准确性对于储能系统的实际应用有一定的工程借鉴价值。
The traditional parameter identification methods for lithium-ion batteries will lead to problems such as low accuracy of identification results and poor adaptability to working conditions
and sudden error and even distortion of output voltage curve due to sudden parameter changes in low SOC region. In this paper
the second-order RC equivalent circuit model is reconstructed according to the different time-scale characteristics of lithium-ion battery dynamics
and it is decomposed into fast dynamic (FD) part and slow dynamic (SD) part. The FD part and SD part of the model are distinguished by the improved IFRLS and the adaptive Kalman filter algorithm (AKF) to avoid the mutual interference between the model parameters. Finally
the proposed method is compared with the traditional parameter identification methods under various working conditions
and the results prove the effectiveness and accuracy of the proposed parameter identification method
which has certain engineering reference value for the practical application of energy storage system.
李练兵, 朱乐, 李思佳, 等. 基于差分电压和ICS-Elman神经网络的锂离子电池剩余使用寿命预测方法[J]. 太阳能学报, 2023, 44(12): 433-443.
何冰琛, 杨薛明, 王劲松, 等. 基于PCA-GPR的锂离子电池剩余使用寿命预测[J]. 太阳能学报, 2022, 43(5): 484-491.
刘玉洁, 赵巍, 孙孝峰, 等. 储能系统锂离子电池附加受控电压源等效电路模型研究[J]. 太阳能学报, 2023, 44(8): 1-9.
黄凯, 郭永芳, 李志刚. 基于信息反馈粒子群的高精度锂离子电池模型参数辨识[J]. 电工技术学报, 2019, 34(S1): 378-387.
朱瑞, 段彬, 温法政, 等. 基于分布式最小二乘法的锂离子电池建模及参数辨识[J]. 机械工程学报, 2019, 55(20): 85-93.
YANG Z, WANG X M.An improved parameter identification method considering multi-timescale characteristics of lithium-ion batteries[J]. Journal of energy storage, 2023, 59: 106462.
DAI H F, XU T J, ZHU L T, et al.Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales[J]. Applied energy, 2016, 184: 119-131.
HU Y R, WANG Y Y.Two time-scaled battery model identification with application to battery state estimation[J]. IEEE transactions on control systems technology, 2015, 23(3): 1180-1188.
ZHANG C, ALLAFI W, DINH Q, et al.Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique[J]. Energy, 2018, 142: 678-688.
罗勇, 祁朋伟, 阚英哲, 等. 基于模拟退火算法的锂电池模型参数辨识[J]. 汽车工程, 2018, 40(12): 1418-1425.
巫春玲, 胡雯博, 孟锦豪, 等. 基于最大相关熵扩展卡尔曼滤波算法的锂离子电池荷电状态估计[J]. 电工技术学报, 2021, 36(24): 5165-5175.
项宇, 刘春光, 李嘉麒. 基于卡尔曼滤波的锂离子电池模型参数辨识[J]. 兵器装备工程学报, 2016, 37(10): 147-151.
ZHENG F D, XING Y J, JIANG J C, et al.Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries[J]. Applied energy, 2016, 183: 513-525.
谈发明, 赵俊杰, 李秋烨. 基于简化滞回OCV模型的锂电池SOC自适应估计策略[J]. 中国电机工程学报, 2021, 41(2): 703-715.
范兴明, 封浩, 张鑫. 最小二乘算法优化及其在锂离子电池参数辨识中的应用[J]. 电工技术学报, 2024, 39(5): 1577-1588.
REN B Y, XIE C X, SUN X D, et al.Parameter identification of a lithium-ion battery based on the improved recursive least square algorithm[J]. IET power electronics, 2020, 13(12): 2531-2537.
程燕, 王海峰, 王学运, 等. 复杂环境下基于自适应卡尔曼滤波的时间比对跟踪算法[J]. 电子与信息学报, 2023, 45(11): 4110-4116.
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