Abstract
We present a physics-informed neural ordinary differential equation (ODE) based model to determine the temperature in electric motor permanent magnets. The input-to-state stability (ISS) property is ensured by the model's architecture independently of the model's parameters. This improves the model's safety and contributes to easy deployment as a virtual sensor. We train this model on a real-world data set and demonstrate its advantages over a nonlinear autoregressive exogenous (NARX) neural network.
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