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Design and Identification of a Lumped-Parameter Thermal Network for Permanent Magnet Synchronous Motors Based on Heat Transfer Theory and Particle Swarm Optimis
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| Author(s) |
Oliver WALLSCHEID |
| Abstract |
A lumped-parameter thermal network (LPTN) for a permanent magnet motor (PMSM) is developed to estimate the most crucial motor temperatures. In this contribution a 60 kW PMSM prototype designed for automotive traction drives is used as the investigation basis. Aiming at real-time motor monitoring well-known analytic equations from the heat transfer theory are used to model the dominant heat paths. Based on a three-dimensional approach in cylindrical coordinates a differential-algebraic state-space model with varying parameters (LPV) is proposed. Due to the chosen level of model abstraction as well as motor material data uncertainties signi_cant estimation errors between the LPTN and experimental test bench measurements result. To improve the estimation accuracy particle swarm optimisation (PSO) is applied for strategic _tting of uncertain model parameters with respect to a maximum likelihood cost function. To avoid converging in suboptimal local minima, which is a typical problem of gradient-based standard optimisation methods, the meta-heuristic PSO is utilised for the resulting multi-variable, non-linear and constrained optimisation problem. For the identi_cation process experimental training data is used which is statistically independent from the (cross-)validation pro_les. As a result the maximum estimation error (worst-case) regarding all considered motor component temperatures can be drastically reduced to 8 °C. |
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| Filename: | 0711-epe2015-full-15551314.pdf |
| Filesize: | 465 KB |
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| Type |
Members Only |
| Date |
Last modified 2016-06-08 by System |
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