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| 基于EKF-MIUKF的锂电池SOC在线估算 |
| Online SOC estimation of lithium-ion batteries based on EKF-MIUKF |
| 投稿时间：2024-06-09 |
| DOI：10.13259/j.cnki.eri.2026.01.007 |
| 中文关键词: 锂离子电池 荷电状态(SOC) 扩展卡尔曼滤波（EKF） 多新息无迹卡尔曼滤波（MIUKF） |
| 英文关键词:lithium-ion battery state of charge (SOC) extended Kalman filter (EKF) multi-innovation unscented Kalman filter (MIUKF) |
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| 中文摘要: |
| 电池荷电状态（SOC）精确估计对提高新能源汽车电池管理系统的性能、电池使用安全性以及整车能量管理策略有效性具有至关重要的作用。采用二阶RC等效电路模型描述锂离子电池的动态特性，并通过实验获取SOC与开路电压（OCV）之间的关系。在SOC估算过程中，为克服参数辨识中对噪声敏感的问题，运用扩展卡尔曼滤波（EKF）算法在线辨识模型参数；为实现对历史数据的重复使用，有效提高无迹卡尔曼滤波（UKF）在非线性系统中的适应性与收敛性，采用多新息无迹卡尔曼滤波（MIUKF）算法，并对MIUKF进行了分析与模型搭建，在城市道路行驶循环（UDDS）工况下对模型参数和SOC进行验证，并将结果与EKF、UKF算法的结果进行对比。结果表明，采用MIUKF算法估计锂电池SOC的误差控制在0.78%左右，估算误差小，鲁棒性好。 |
| 英文摘要: |
| Accurate estimation of the state of charge (SOC) of batteries is crucial for enhancing the performance of battery management systems in new energy vehicles, ensuring battery safety, and improving the accuracy of vehicle energy management strategies. This paper employs a second-order RC equivalent circuit model to characterize the dynamic behavior of lithium-ion batteries and experimentally establishes the relationship between SOC and open-circuit voltage (OCV). During the SOC estimation process, the extended Kalman filter (EKF) algorithm is employed for online identification of model parameters to mitigate noise sensitivity issues. To facilitate the reuse of historical data and enhance the adaptability and convergence of the unscented Kalman filter (UKF) in nonlinear systems, the multi-innovation unscented Kalman filter (MIUKF) algorithm is adopted. The MIUKF algorithm is analyzed and the corresponding model is constructed. Model parameters and SOC are validated using the UDDS driving cycle, and the results are compared with those obtained from EKF and UKF algorithms. The results demonstrate that the MIUKF algorithm estimates the SOC of lithium-ion batteries with an error of approximately 0.78%, exhibiting low estimation error and good robustness. |
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