This paper presents a multilevel model-based hierarchical estimation framework for complex thermal management systems of electrified vehicles. System dynamics are represented by physics-based lumped parameter models derived from a graph-based modeling approach. The complexity of the hierarchical models is reduced by applying an aggregation-based model-order reduction technique that preserves the physical correspondence between a reduced-order model and the physical system. This paper also presents a case study in which a hierarchical observer is designed to estimate the dynamics of a candidate system. The hierarchical observer is connected to a previously developed hierarchical controller for closed-loop control, and the closed-loop performance is demonstrated through simulation and real-time experimental results. A comparison between the proposed hierarchical observer and a centralized observer shows the tradeoff between the estimation accuracy and the computational complexity of the two approaches.