Abstract:

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 the existence of system 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 for estimating the temperature field of LiBs. First, we demonstrate that the evolution of a pouch-type LiB cell’s temperature can be formulated in a physically consistent manner as a spatio-temporal random field. Then, we leverage the Kridged Kalman filter to update and reconstruct the random temperature field sequentially through time. Our simulations show that the proposed approach can provide accurate reconstruction of a LiB cell’s temperature field with a small number of sensors.