Rising vehicle complexity and electrification increase the thermal loads on
automotive components, making reliable temperature models essential for ensuring
thermal operational safety over the vehicle lifetime. Existing approaches
(experimental wind tunnel testing, numerical simulation, and purely data-driven
methods) lack scalability to many operating conditions, do not provide
physically interpretable parameters, or yield inconsistent results when applied
across multiple experiments. This paper addresses the gap of fitting a single,
physics-constrained temperature model simultaneously across multiple
experimental measurements, enabling consistent parameter estimation and
prediction of unseen operating conditions. A lumped parameter thermal network
(LPTN) is parameterized using a global minimization approach that classifies
each model coefficient as global, discrete-global, or local, depending on
whether it is shared across all measurements, across a subset with the same
design configuration, or varies individually. The method is evaluated on an
electronic control unit (ECU) installed in the BMW 7 Series, using nine wind
tunnel measurements covering three different cooling strategies (ventilation,
heat pipe, metal insert). A single global model fitted to six measurements
achieves a root-mean-square error (RMSE) of 1.09 K, while three
unseen measurements are predicted with an RMSE of 1.19 K.
Compared to conventional single-measurement fitting, global estimation reduces
convergence time to 21.2%, while yielding physically interpretable and
consistent parameters across experiments. These results demonstrate that global
LPTN parameter estimation provides a fast, robust, and physically interpretable
framework for automotive thermal operational safety, capable of reliable
extrapolation to unseen conditions with sparse experimental data.