Sensorless Battery Internal Temperature Estimation using a Kalman Filter with Impedance Measurement

TL;DR

This paper introduces a sensorless method using an extended Kalman filter and impedance measurements to accurately estimate both core and surface temperatures of lithium-ion batteries, eliminating the need for direct temperature sensors.

Contribution

It develops a thermal model coupled with impedance measurement for internal temperature estimation, demonstrating comparable accuracy to traditional sensor-based methods.

Findings

Accurately estimates core and surface temperatures without direct sensors.

Capable of estimating the convection coefficient at the cell surface.

Performance comparable to conventional methods using surface temperature sensors.

Abstract

This study presents a method of estimating battery cell core and surface temperature using a thermal model coupled with electrical impedance measurement, rather than using direct surface temperature measurements. This is advantageous over previous methods of estimating temperature from impedance, which only estimate the average internal temperature. The performance of the method is demonstrated experimentally on a 2.3 Ah lithium-ion iron phosphate cell fitted with surface and core thermocouples for validation. An extended Kalman filter, 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) based on the same 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 as the measurement input is comparable to an equivalent dual Kalman filter using a conventional surface temperature sensor as measurement input.