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Open AccessArticle

Sensitivity Analysis of the Battery Model for Model Predictive Control: Implementable to a Plug-In Hybrid Electric Vehicle

1
Energy Production and Infrastructure Center, University of North Carolina, Charlotte, NC 28223, USA
2
Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39759, USA
3
Electrical and Computer Engineering Department, California State University, Los Angeles, CA 90032, USA
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2018, 9(4), 45; https://doi.org/10.3390/wevj9040045
Received: 11 September 2018 / Revised: 29 October 2018 / Accepted: 30 October 2018 / Published: 6 November 2018
Developing an efficient online predictive modeling system (PMS) is a major issue in the field of electrified vehicles as it can help reduce fuel consumption, greenhouse gasses (GHG) emission, but also the aging of power-train components, such as the battery. For this manuscript, a model predictive control (MPC) has been considered as PMS. This control design has been defined as an optimization problem that uses the projected system behaviors over a finite prediction horizon to determine the optimal control solution for the current time instant. In this manuscript, the MPC controller intents to diminish simultaneously the battery aging and the equivalent fuel consumption. The main contribution of this manuscript is to evaluate numerically the impacts of the vehicle battery model on the MPC optimal control solution when the plug hybrid electric vehicle (PHEV) is in the battery charge sustaining mode. Results show that the higher fidelity model improves the capability of accurately predicting the battery aging. View Full-Text
Keywords: battery modeling; hybrid electric vehicle; model predictive control; optimization algorithm; sensitivity analysis battery modeling; hybrid electric vehicle; model predictive control; optimization algorithm; sensitivity analysis
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Sockeel, N.; Shi, J.; Shahverdi, M.; Mazzola, M. Sensitivity Analysis of the Battery Model for Model Predictive Control: Implementable to a Plug-In Hybrid Electric Vehicle. World Electr. Veh. J. 2018, 9, 45.

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