Optimal Parameter Calibration for Physics Based Multi-Mass Engine Model

Paper #:
  • 2017-01-0214

Published:
  • 2017-03-28
Abstract:
Designing an efficient transient thermal system model has become a very important task in improving fuel economy. As opposed to steady-state thermal models, part of the difficulty in designing a transient model is optimizing a set of inputs. The first objective in this work is to develop an engine compatible physics-based 1D thermal model for fuel economy and robust control. In order to capture and study the intrinsic thermo-physical nature, both generic “Three Mass” and “Eight Mass” engine model are developed. The models have been correlated heuristically using Simulink and Flowmaster, respectively. In order to extend the lumped mass engine model it also has been extended to Simulink model. In contrast to the complexity of the models the “Heuristic search” of input parameters has been found to be challenging and time consuming. Hence, in this work a Particle Swarm Optimizer (PSO) method has been introduced and implemented on a simple 3-mass and more complex 8-mass engine thermal model in order to optimize the input parameters. PSO has been proven to be effective in handling a large set of parameters which need calibration (i.e. optimization). These parameters were optimized and validated over different transient drive cycles: both fuel economy and extreme cases. Results demonstrate that the use of the PSO guarantees a better correlation of the transient models to vehicle level test data, while providing a systematic way to find an optimal set of parameters for the transient model. The generic framework can be extended to different vehicle thermo-mechanical components such as transmission or heat-exchangers.
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