Spatio-Temporal Thermal Monitoring for Lithium-Ion Batteries via Kriged Kalman Filtering

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.