| 郭向伟,李璐颖,王晨,王亚丰,李万.自适应渐消无迹卡尔曼滤波锂电池SoC估计[J].电子测量与仪器学报,2024,38(3):167-175 |
| 自适应渐消无迹卡尔曼滤波锂电池SoC估计 |
| SoC estimation of lithium battery based on adaptivefading unscented kalman filter |
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| DOI： |
| 中文关键词: 荷电状态 衰减因子 无迹卡尔曼滤波 自适应渐消无迹卡尔曼滤波 |
| 英文关键词:SoC fading factor UKF AFUKF |
| 基金项目:国家自然科学基金(61973105)、河南省高校基本科研业务费(NSFRF210332, NSFRF230604)、河南省高校重点科研项目(23A470006)、河南省科技攻关项目(232102240078)资助 |
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| Author | Institution |
| Guo Xiangwei | School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China |
| Li Luying | School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China |
| Wang Chen | School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China |
| Wang Yafeng | School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China |
| Li Wan | School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China |
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| 中文摘要: |
| 精确的荷电状态(SoC)是锂电池安全高效运行的重要保障, 文章针对传统无迹卡尔曼滤波(UKF)对非线性系统突变状态跟踪能力差, 导致SoC估计精度低的问题, 提出一种新型自适应渐消无迹卡尔曼滤波(AFUKF)SoC估计方法。首先, 通过设计新型衰减因子对UKF误差协方差矩阵进行加权, 并基于新型衰减因子完成AFUKF的设计, 减小陈旧量测值对估计结果的影响, 提高传统UKF的估计精度和跟踪能力。其次, 基于自主实验平台测试数据, 验证了本文所提AFUKF算法存在初始误差时, 相较于传统UKF算法, ECE工况下平均绝对误差和均方根误差分别下降了47.95%和33.92%, DST工况下分别下降了36.40%和27.73%; 相较于同类改进的AUKF算法, ECE工况下平均绝对误差和均方根误差分别下降了43.36%和33.51%, DST工况下分别下降了39.01%和25.63%。模型结果表明, 相比于传统UKF算法以及同类型改进的AUKF算法, AFUKF具有更高的估计精度, 且在相同初始SoC误差条件下具有更好的鲁棒性。 |
| 英文摘要: |
| Accurate SoC is an important guarantee for the safe and efficient operation of lithium batteries. Aiming at the problem that the traditional unscented Kalman filter (UKF) has poor tracking ability for the abrupt state of nonlinear systems, which in turn leads to the low accuracy of SoC estimation, a new adaptive fading unscented Kalman filter was proposed for SoC estimation in this paper. First, the UKF error covariance matrix is weighted by designing a novel fading factor, and the design of the AFUKF is completed based on the novel fading factor, which reduces the influence of stale measurements on the estimation results, improves the estimation accuracy and tracking ability of the traditional UKF. Second, based on the test data of the self-built experimental platform, it is verified that the AFUKF proposed in this paper, in the presence of the initial error, compared with the traditional UKF, the mean absolute error (MAE) and root-mean-square error (RMSE) under the ECE condition are decreased by 47.95% and 33.92%, respectively, the MAE and RMSE under the DST condition are decreased by 36.40% and 27.73%, respectively. Compared with the similar improved AUKF, the MAE and EMSE decreased by 43.36% and 33.51% for the ECE condition, 39.01% and 25.63% for the DST condition, respectively. The modeling results show that, AFUKF has higher accuracy and better robustness under initial SoC errors than the traditional UKF as well as the improved AUKF of the same type. |
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