Spatio-Temporal Thermal Monitoring for Lithium-Ion Batteries via Kriged Kalman Filtering
Spatio-Temporal Thermal Monitoring for Lithium-Ion Batteries via Kriged Kalman Filtering
复制标题
DOI：
10.1109/cdc51059.2022.9992543
复制
发表时间：
2022-12
期刊：
影响因子：
--
通讯作者：
H. Tu;Yebin Wang;Xianglin Li;H. Fang
中科院分区：
文献类型：
--
作者：
H. Tu;Yebin Wang;Xianglin Li;H. Fang
热监测在确保锂离子电池（LiB）安全、高效和持久运行方面发挥着至关重要的作用。文献中现有的方法大多依赖于基于物理的热模型。然而，由于各种不确定性，如未捕获的动态，参数误差和未知的冷却条件，实际上很难获得准确的物理热模型。针对这一问题，本文提出了一种基于数据驱动的Kriged Kalman滤波方法来估计LiBs的温度场。首先，我们证明了袋型LiB电池的温度场的演化可以用物理上一致的方式表示为时空随机场。然后，我们利用克里格卡尔曼滤波器更新和重建的随机温度场顺序通过时间使用传感器数据。我们的模拟表明，该方法可以准确地重建LiB电池的温度场与少量的传感器。
Thermal monitoring plays an essential role in ensuring safe, efficient and long-lasting operation of lithium-ion batteries (LiBs). Existing methods in the literature mostly rely on physics-based thermal models. However, an accurate physical thermal model is practically hard to obtain due to various uncertainties such as uncaptured dynamics, parameter errors, and unknown cooling conditions. Motivated by this problem, this paper considers a data-driven approach named Kriged Kalman filter to estimate the temperature field of LiBs. First, we demonstrate that the evolution of a pouch-type LiB cell’s temperature field can be formulated as a spatio-temporal random field in a physically consistent manner. Then, we leverage the Kriged Kalman filter to update and reconstruct the random temperature field sequentially through time using sensor data. Our simulations show that the proposed approach can accurately reconstruct the LiB cell’s temperature field with a small number of sensors.