Nonlinear Model Predictive Control of a Power-Split Hybrid Electric Vehicle with Electrochemical Battery Model
Department of Electrical and Computer Engineering
This paper studies the nonlinear model predictive control for a power-split Hybrid Electric Vehicle (HEV) power management system to improve the fuel economy. In this paper, a physics-based battery model is built and integrated with a base HEV model from Autonomie®, a powertrain and vehicle model architecture and development software from Argonne National Laboratory. The original equivalent circuit battery model from the software has been replaced by a single particle electrochemical lithium ion battery model. A predictive model that predicts the driver's power request, the battery state of charge (SOC) and the engine fuel consumption is studied and used for the nonlinear model predictive controller (NMPC). A dedicated NMPC algorithm and its solver are developed and validated with the integrated HEV model. The performance of the NMPC algorithm is compared with that of a rule-based controller. This study provides a sound basis for the further study of stochastic MPC and NMPC for the HEV power management with the consideration of battery aging and thermal performance.
SAE Technical Papers
Nonlinear Model Predictive Control of a Power-Split Hybrid Electric Vehicle with Electrochemical Battery Model.
SAE Technical Papers,
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/2993
© 2017 SAE International. Publisher’s version of record: https://doi.org/10.4271/2017-01-1252