## 1 Introduction and Related Work
In addition to determining the thermal parameters, the coefficients for loss distributions are tuned in the optimization process to account for the loss-related uncertainties. As described by [PERSON] [7], parameter identification can be performed in two ways: a local approach or a global approach. In the local approach, the scheduling variables that influence the thermal parameters are kept constant during the measurements. [PERSON] et al. [12] presented a local approach for parametrizing a three-node LPTN model. A sequence of independent temperature measurement data is stored, varying the model inputs while keeping the scheduling variable constant. Each set of measurement data represents a linear time-invariant model cycle. The corresponding loss inputs are calculated with the available motor electrical control unit data such as motor speed and electric currents. The parameter identification approach was built on the idea of mapping the linear time-varying parameters to a set of time-invariant models operating within the boundary of the scheduling variable. Thus, a consistent parameter set for the whole operating region could be obtained with the adaptation of the relevant boundary conditions through various identification cycles. In contrast to the local approach, in the global approach, both the scheduling variables and the model inputs are simultaneously varied. Thus, the parameters are identified utilizing the comprehensive experimental data, leading to more robust results. [PERSON] [14] used a global approach to identifying the thermal parameters of a four-node LPTN model and the coefficients for the loss distribution. Extensive power-loss measurements were made in the complete operation range while keeping the temperature constant at the nodes. Additionally, the loss distribution among the nodes is interpreted as part of the parameter identification procedure for dealing with the uncertainty. A combination of PSO; a metaheuristic optimization method; and a gradient-based, sequential quadratic programming technique is used to deal with the nonlinear dependence of the parameters on the schedule variables. While the global approach is more robust than the local approach in capturing all operating regions of the motor, it can also be problematic if the parameter landscape to be identified is large and highly nonlinear in nature [7]. [PERSON] and [PERSON] [15] in their work present an online measurement-informed thermal parameter estimation using a recursive Kalman filter method. While a pulse width modulation-based estimation method is utilized for rotor temperature measurement, the temperatures in three nodes such as stator core, winding, and rotor are predicted. The input losses for the LPTN model are derived based on a model-based approach and with the use of FEA. The identification problem is formulated as a state observer with eight states and an Extended Kalman Filter is used to address the nonlinearity in the model. Three of the states correspond to the nodes' temperatures and the other five states represent the unknown thermal resistance parameters in the LPT network. Though viable, the grey-box LPTNs require excessive motor measurement data to train a drastically reduced model order. However, the question remains whether such a high level of abstraction can accurately capture the nonlinearity and detail loss characteristics in a complex drive system application. Nonetheless, rapid progress in the applicability of machine learning (ML) techniques in varied domains and advancement in specialized embedded hardware platforms in the present scenario revolutionized the way temperature monitoring is performed. ML techniques are purely data-driven and employ neural networks to mimic the physical behavior of the involved system. A machine learning model can be trained offline or online empirically using data collected from test benches to parametrize a thermal model and estimate temperatures.