Rapid Modeling of Transient Thermal Systems through Ensemble Parameter Estimation Applied to the Lumped Parameter Linear Superposition Method

Efficient thermal modeling is essential for the design and reliability of power electronics systems, particularly under fast transient operating conditions. Building upon prior formulations of the Lumped Parameter Linear Superposition (LPLSP) method, this work introduces an ensemble parameter estimation framework that enables reduced-order thermal model generation from a single transient dataset. Unlike the earlier implementation that relied on multiple parametric simulations to excite each heat source independently, the proposed approach simultaneously identifies all model coefficients using fully transient excitations. Two estimation strategies namely two-stage decomposition and rank reduction are developed to further reduce computational cost and improve scalability for larger systems. The proposed strategies yield models with temperature-prediction errors within 5% of CFD simulations while reducing model development times from O(103 s) to O(100 s)–O(101 s). Once constructed, the model evaluates new transient operating conditions in O(100 s), enabling rapid thermal analysis and enabling automated generation of reduced-order models for both simulated and physical systems. These advancements significantly accelerate early stage design iterations and mission-profile evaluations for thermal management of electronic systems.