Online Temperature Estimation of Li-ion Battery Pack Using Principal Component Analysis

Abstract

Thermal management is one of the important function of the battery management system (BMS). The thermal management system monitors and equalizes the temperature distribution of the battery pack to prevent the different cell degradation rate and to keep the battery on its best
performance. In this study, as a part of the thermal management system the in-situ temperature estimation method is developed based on the principal component analysis (PCA) reinforced with the measured temperatures. To begin with, the PCA is used for finding the basis vectors
of the battery thermal system, which is the eigenvectors of the covariance matrix of the training data set. Then an arbitrary thermal map can be expressed as the linear combination of these basis vectors and their amplitudes. The amplitude for each basis vectors is estimated from the
measured temperatures. The performance of the thermal map reconstruction depends on the accuracy of this amplitude estimation which again is related to the temperature measurement locations. The measured locations are determined considering two aspects: the prediction accuracy
and the robustness of the sensor network. To find the sensor location satisfying both criteria, the sensor network optimization problem is accordingly formulated, and solved by the genetic algorithm. The proposed study is validated for various operating conditions including the distributed heat generation condition and different cooling conditions.