Data-driven Modeling of Thermal Fuses

Paper #:
  • 2018-01-0768

Published:
  • 2018-04-03
Abstract:
Both the integration of safety-critical electrical systems and the increasing power requirements in vehicles present a challenge for electrical distribution systems in terms of reliability, packaging, weight, and cost. In this regard, the wire protection device is a key element, as it determines the reliability of the short circuit detection, the immunity against false tripping, and the wire diameters. Currently, in most cases, thermal fuses are used, due to their low cost and robust design. However, the description of their tripping behavior based only on steady-state currents is insufficient for the increasingly complex current profiles in vehicles. Thus, in order to achieve an optimum dimensioning of a fuse-wire combination, a profound understanding of the thermal behavior of both components under dynamic load conditions is mandatory. However, the FEM tools used for the thermal design of fuses are relatively slow, require huge calculation resources, and must be well parameterized. The presented approach is a data-driven fuse model. An experimental set-up allows a precise temperature measurement based on IR-thermography and reference sensors for a set of fuses, which were modified in order to have access to their internal hot spots. By applying a fixed electrical current, the thermal response can be determined and be described by a set of four PT1 elements. Because of a nonlinear dependency between current and this response function, the model becomes more complex and must be based on several measurement data sets for various currents. Using a recursive approach, a new standard model has been developed which can accurately be parameterized for various fuse types and diverse nominal currents and allows the calculation of the maximum temperature within the fuse for any given current-time profile. The paper describes the measurement set-up, the model and its parameterization method, and a comparison of calculated and experimental data.
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