| 引用本文: | 杨 翀1
,凌六一1,2.基于 FOUKF-FOSUKF 的锂电池 SOC 估计(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2025，42（5）：107-113 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 对锂电池荷电状态的准确估计在新能源汽车领域具有重要意义,基于此,提出一种双无迹卡尔曼滤波
算法(FOUKF-FOSUKF),即分数阶球形无迹卡尔曼滤波算法和分数阶无迹卡尔曼滤波算法联合估计电池荷电状
态的方法。 方法 先用自适应遗传算法离线辨识电池模型的参数;再用分数阶无迹卡尔曼滤波算法(FOUKF)进行
在线参数辨识,实时估计并更新锂电池分数阶模型中的各个参数;最后利用所提出的联合算法 FOUKF-FOSUKF 对
锂电池的荷电状态进行估计,在动态应力测试和 US06 两种工况下与传统整数阶球形无迹卡尔曼滤波算法(SUKF)
和分数阶球形卡尔曼滤波算法(FOSUKF)进行精度验证对比。 结果 在估计荷电状态的过程中,FOUKF-FOSUKF
的 SOC 误差和电压误差均远低于传统的 SUKF 与 FOSUKF,该算法可以有效估计电池模型中的参数,降低端电压
估计的误差,提高估计荷电状态的精度。 结论 FOUKF-FOSUKF 在估计锂电池荷电状态方面对比 SUKF 和 FOSUKF
算法具有精度更高,误差更小,适用性更强,收敛性更好的优点。 |
| 关键词: 荷电状态 分数阶模型 自适应遗传算法 分数阶球形无迹卡尔曼滤波 |
| DOI： |
| 分类号: |
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| State of Charge Estimation of Lithium Battery Based on FOUKF-FOSUKF |
|
YANG Chong
1
LING liuyi
1 2
|
|
1. School of Electrical and Information Engineering Anhui University of Science and Technology Anhui Huainan
232001 China
2. School of Artificial Intelligence Anhui University of Science and Technology Anhui Huainan 232001 China
|
| Abstract: |
| Objective Accurate estimation of the state of charge SOC of lithium batteries is of great significance in the
field of new energy vehicles. Therefore a dual unscented Kalman filter algorithm FOUKF-FOSUKF is proposed which
combines the fractional-order spherical unscented Kalman filter FOUKF and the fractional-order unscented Kalman filter
FOSUKF to estimate the SOC of batteries. Methods Firstly the parameters of the battery model are identified offline
using the adaptive genetic algorithm AGA . Then the fractional-order unscented Kalman filter FOUKF is employed
for online parameter identification to estimate and update the parameters in the fractional-order model of the lithium battery
in real-time. Finally the proposed combined algorithm FOUKF-FOSUKF is used to estimate the state of charge SOC
of the lithium battery. The accuracy of the proposed method is verified and compared with the traditional integer-order
spherical unscented Kalman filter SUKF and the fractional-order spherical Kalman filter FOSUKF under two
conditions dynamic stress test DST and US06. Results During the SOC estimation process the SOC error and voltage
error of FOUKF-FOSUKF are significantly lower than those of the traditional SUKF and FOSUKF. This algorithm can
effectively estimate the parameters in the battery model reduce the error in terminal voltage estimation and improve the
accuracy of SOC estimation. Conclusion FOUKF-FOSUKF has higher accuracy smaller error stronger applicability
and better convergence compared with the SUKF and FOSUKF algorithms in estimating the SOC of lithium batteries. |
| Key words: state of charge fractional-order model adaptive genetic algorithm fractional-order spherical unscented
Kalman filter |
