Robust Estimation of Battery System Temperature Distribution Under Sparse Sensing and Uncertainty

Abstract

Thermal management is a critical task of battery control to ensure the safe, efficient, and enduring performance of the battery system, which can be considered as an interconnected thermal network of cells. The basis of thermal management is the estimation of temperature and its gradient across the battery system, which has received extensive attention in the literature. However, existing works neglect two important constraints in practical battery systems: 1) limited number of available sensors and 2) presence of system uncertainty such as parameter error. This paper is the first to investigate robust battery system temperature estimation under sparse sensing and system uncertainty. We first propose a framework consisting of optimization problems at three different levels: 1) evaluation of the worst case estimation performance (error) under uncertainty; 2) robust observer design to minimize the worst case error; and 3) optimization of sensor locations. Two robust estimation methods are then used to solve the problem. The system uncertainty considered in this paper is the unknown resistance variability among battery cells, but the methodology can be applied to address other types of uncertainty. It is shown that the designed observers could guarantee and improve the robustness and reliability of estimation by significantly reducing the worst case estimation errors induced by uncertainty.