Sensorless Estimation of Battery Internal Temperature Using Dual Extended Kalman Filter

Subject Areas : Renewable energy

Mohseh  Gholamrezaei ¹ (Department of Electrical/ Islamic Azad university (semnan branch))
Mohammad  Toloo Askari ² (استادیار - گروه مهندسی برق و الکترونیک، واحد سمنان، دانشگاه آزاد اسلامی، سمنان، ایران)

Received: 2019-01-26 Accepted : 2019-04-17 Published : 2019-05-05

Keywords: Battery temperature estimation, Dual extended Kalman filter (DEKF), Electrochemical impedance, Thermal model,

Abstract :

Abstract: The conventional approaches for estimating internal battery temperature use numerical electric-thermal models in which a temperature sensor is required. In order to ensure safe and proper use of lithium-ion batteries during operation, accurate estimation of battery temperature is very important. In this paper, a method for estimating the surface and core temperature of the battery cell is presented using a coupled thermal model with an electrical impedance model without direct measurement of surface temperature. For this purpose, a dual extended Kalman filter (DEKF) consisting of a reduced thermal model along with battery current, voltage and impedance measurement can accurately estimate the temperature of the battery surface and core. The performance of the method is demonstrated experimentally on a 2.3-Ah lithium-ion iron phosphate cell fitted with surface and core thermo-couples for validation. The performance of the method is demonstrated experimentally on a 2.3-Ah lithium-ion iron phosphate cell fitted with surface and core thermo-couples for validation

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