Investigation of Vehicle Speed Prediction from Neural Network Fit of Real World Driving Data for Improved Engine On/Off Control of the EcoCAR3 Hybrid Camaro

Paper #:
  • 2017-01-1262

Published:
  • 2017-03-28
Abstract:
The EcoCAR3 competition challenges student teams to redesign a 2016 Chevrolet Camaro to reduce environmental impacts and increase energy efficiency while maintaining performance and safety that consumers expect of a Camaro. Energy management of the new hybrid powertrain is an integral component of the overall efficiency of the car and is a prime focus of Colorado State University’s (CSU) Vehicle Innovation Team. Previous research has shown that error-less predictions about future driving characteristics can be used to more efficiently manage hybrid powertrains. In this study, a novel real world implementable energy management strategy is investigated for use in the EcoCAR3 Hybrid Camaro. This strategy uses a Nonlinear Autoregressive Neural Network with Exogenous inputs (NARX Net) trained with real world driving data from a selected drive cycle to predict future vehicle speeds along that desired drive cycle. Different prediction windows are analyzed and compared to quantify tradeoffs between prediction window and uncertainty in speed prediction for a given drive cycle. Specifically, a drive cycle similar to what will be driven at EcoCAR3 Year 3’s Final Competition is investigated. To investigate the fuel economy (FE) improvement potential of this new control strategy, a high fidelity model of a Toyota Prius, developed by Colorado State University, is used. An optimal dynamic programming (DP) engine on/off controller is implemented into the Prius model. Several situations are studied for the specified drive cycle: the model baseline controller, a DP derived engine on/off controller using NARX Net speed predictions, and a DP derived engine on/off controller using a 100% accurate speed prediction. These simulation results provide insight into what FE benefit can be expected from speed prediction compared to baseline and idealized conditions. This potential achievable FE benefit is used as motivation to develop a predictive controller that can be implemented in real time on the supervisory controller of CSU’s Plug-in Hybrid Electric Camaro.
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