Document Type
Article
Publication Date
9-22-2022
Department
Department of Mechanical Engineering-Engineering Mechanics; Department of Electrical and Computer Engineering; Department of Applied Computing
Abstract
The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect of additional energy savings for hybrid powertrains. Torque split optimal control methodologies have been a focus in the automotive industry and academia for many years. Their real-time application in modern vehicles is, however, still lagging behind. While conventional exact and non-exact optimal control techniques such as Dynamic Programming and Model Predictive Control have been demonstrated, they suffer from the curse of dimensionality and quickly display limitations with high system complexity and highly stochastic environment operation. This paper demonstrates that Neuroevolution associated drive cycle classification algorithms can infer optimal control strategies for any system complexity and environment, hence streamlining and speeding up the control development process. Neuroevolution also circumvents the integration of low fidelity online plant models, further avoiding prohibitive embedded computing requirements and fidelity loss. This brings the prospect of optimal control to complex multi-physics system applications. The methodology presented here covers the development of the drive cycles used to train and validate the neurocontrollers and classifiers, as well as the application of the Neuroevolution process.
Publication Title
Vehicles
Recommended Citation
Jacquelin, F.,
Bae, J.,
Chen, B.,
Santhosh, P.,
Kraemer, T.,
&
Henderson, B.
(2022).
Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution.
Vehicles,
4(4), 942-956.
http://doi.org/vehicles4040051
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16933
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF