| 摘要: |
| 目的 针对传统卡尔曼滤波算法估算锂电池的荷电状态( SOC) ,其值用 RSOC 准确度不足的问题,提出一种分
数阶鲁棒无迹卡尔曼滤波联合无迹卡尔曼滤波 ( FORUKF - UKF) 方法估计锂电池 SOC。 方法 在动态应力测试
( DST) 下,采用自适应遗传算法( AGA) 对锂电池分数阶模型( FOM) 进行参数辨识;在 FOM 的基础上将无迹变换
(UT) 技术与 H∞ 观测器结合提出 FORUKF 算法,并与 UKF 联合实现 SOC 估计;联合估计器中的 UKF 实时估计电
池模型中的欧姆电阻 R0 ,并反馈至 FORUKF 算法中估算得到 SOC;最后在北京动态应力测试( BJDST) 下与拓展卡
尔曼滤波( EKF) 、分数阶无迹卡尔曼滤波( FOUKF) 进行比较验证。 结果 在估计 SOC 的过程中 FORUKF-UKF 方法
相对于 EKF、FOUKF 和 FORUKF 始终保持了最高的估计精度,展现了更好的鲁棒性。 结论 FORUKF-UKF 方法在
估计锂电池 SOC 方面比 EKF、FOUKF 和 FORUKF 算法具备更好的准确性和鲁棒性。 |
| 关键词: 荷电状态 自适应遗传算法 分数阶模型 分数阶鲁棒无迹卡尔曼滤波 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Research on the Joint Estimation of Lithium Battery SOC Based on FORUKF-UKF |
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LUO Wenfei XING Likun
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School of Electrical and Information Engineering Anhui University of Science and Technology Anhui Huainan 232001
China
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| Abstract: |
| Objective In order to address the issue of inadequate accuracy in estimating the state of charge SOC of
lithium batteries using traditional Kalman filtering algorithms this study proposes a fractional order robust unscented
Kalman filter-based unscented Kalman filter FORUKF-UKF method for SOC estimation. The estimated value of the
lithium battery?? s state of charge is denoted by RSOC . Methods An adaptive genetic algorithm AGA was employed to
identify the parameters of a fractional order model FOM of the lithium battery during dynamic stress testing DST . The
FORUKF algorithm was proposed by combining the unscented transform UT technique with the H∞ observer based on
FOM and the SOC estimation was jointly realized with the UKF. The UKF in the joint estimator realized real-time
estimation of the Ohmic resistance R0 in the battery model and fed it back to the FORUKF algorithm to estimate SOC.
Finally comparisons and verifications were conducted with extended Kalman filtering EKF and fractional order
unscented Kalman filtering FOUKF using Beijing dynamic stress testing BJDST . Results The results showed that the
FORUKF-UKF method consistently achieved the highest estimation accuracy in the SOC estimation process compared with EKF FOUKF and FORUKF demonstrating better robustness. Conclusion The FORUKF-UKF method has better
accuracy and robustness than the EKF FOUKF and FORUKF algorithms in estimating the SOC of lithium batteries. |
| Key words: state-of-charge adaptive genetic algorithm fractional order model fractional order robust unscented Kalman
filter |
