Lithium ion batteries should always be prevented from overheating and, hence, thermal monitoring is indispensable. Since only the surface temperature of the battery can be measured, a thermal model is needed to estimate the core temperature of the battery, which can be higher and more critical. In this paper, an online parameter identification scheme is designed for a cylindrical lithium ion battery. An adaptive observer of the core temperature is then designed based on the online parameterization methodology and the surface temperature measurement. A battery thermal model with constant internal resistance is explored first. The identification algorithm and the adaptive observer is validated with experiments on a 2.3Ah 26650 lithium iron phosphate/graphite battery. The methodology is later extended to address temperature-dependent internal resistance with nonuniform forgetting factors. The ability of the methodology to track the long-term variation of the internal resistance is beneficial for battery health monitoring.

The estimation of temperature inside a battery cell requires accurate information about the cooling conditions even when the battery surface temperature is measured. This paper presents a model-based approach for estimating temperature distribution inside a cylindrical battery under unknown convective cooling conditions. A reduced-order thermal model using a polynomial approximation of the temperature profile inside the battery is used. A dual Kalman filter (DKF), a combination of a Kalman filter and an extended Kalman filter, is then applied for the identification of the convection coefficient and the estimation of the battery core temperature. The thermal properties are modeled by volume averaged lumped-values under the assumption of a homogeneous and isotropic volume. The model is parameterized and validated using experimental data from a 2.3 Ah 26650 lithium-iron-phosphate battery cell with a forced-air convective cooling during hybrid electric vehicle drive cycles. Experimental results show that the proposed DKF-based estimation method can provide an accurate prediction of the core temperature under unknown cooling conditions by measuring battery current and voltage along with surface and ambient temperatures.