Lithium-ion batteries have become the preferred energy storage component for
electric vehicles due to their excellent overall performance. However, during
use, they generate heat, causing the battery temperature to rise and the
internal and surface temperatures to be inconsistent, affecting the battery’s
performance and even leading to thermal safety issues. It is difficult to obtain
real-time internal temperature measurements in actual vehicles. Therefore,
accurately estimating the internal temperature of the battery, promptly
detecting thermal faults, and ensuring efficient and safe operation of the
battery are of great importance. This paper establishes a dual-state thermal
model based on extended Kalman filtering for a square ternary lithium battery,
which achieves real-time updating of external thermal resistance and online
estimation of core battery temperature. For this type of lithium battery and its
battery module, an experimental platform was set up, and basic performance
experiments were designed to identify the thermal physical parameters of the
battery and dynamic condition experiments to evaluate the performance of the
model. The results show that the established dual-state thermal model has an
absolute temperature error of less than 0.7°C under constant power conditions.
Under different operating conditions and temperature conditions, the absolute
error in the estimated core temperature of the battery pack is within 1.3°C, and
the relative error is within 2.822%, proving that the method has high accuracy
and good robustness.