Journal
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
Volume 22, Issue 6, Pages 2277-2286Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCST.2014.2309492
Keywords
Dual Kalman filter (DKF); lithium ion (Li-ion) batteries; reduced-order model; state and parameter estimation; thermal modeling
Funding
- Automotive Research Center [W56HZV-04-2-0001]
- U.S. Army Tank Automotive Research, Development and Engineering Center, Warren, MI, USA
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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 26 650 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.
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