Sensorless Battery Internal Temperature Estimation Using a Kalman Filter With Impedance Measurement PROJECT TITLE :Sensorless Battery Internal Temperature Estimation Using a Kalman Filter With Impedance MeasurementABSTRACT:This study presents a method of estimating battery- cell core and surface temperature employing a thermal model let alone electrical impedance measurement, rather than using direct surface temperature measurements. This can be advantageous over previous methods of estimating temperature from impedance, that only estimate the common internal temperature. The performance of the strategy is demonstrated experimentally on a a pair of.3-Ah lithium-ion iron phosphate cell fitted with surface and core thermocouples for validation. An extended Kalman filter (EKF), consisting of a reduced-order thermal model coupled with current, voltage, and impedance measurements, is shown to accurately predict core and surface temperatures for a current excitation profile based on a vehicle drive cycle. A dual-extended Kalman filter (DEKF) primarily based on the identical thermal model and impedance measurement input is capable of estimating the convection coefficient at the cell surface when the latter is unknown. The performance of the DEKF using impedance because the measurement input is corresponding to a similar twin Kalman filter (DKF) employing a conventional surface temperature sensor as measurement input. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest A Privacy-Aware Authentication Scheme for Distributed Mobile Cloud Computing Services Scheduled Perturbation to Reduce Nondetection Zone for Low Gain Sandia Frequency Shift Method
