Predictive control of a power-split HEV with fuel consumption and SOC estimation
Department of Mechanical Engineering-Engineering Mechanics; Center for Cyber-Physical Systems
This paper studies model predictive control algorithm for Hybrid Electric Vehicle (HEV) energy management to improve HEV fuel economy. In this paper, Model Predictive Control (MPC), a predictive control method, is applied to improve the fuel economy of powersplit HEV. A dedicated model predictive control method is developed to predict vehicle speed, battery state of charge (SOC), and engine fuel consumption. The power output from the engine, motor, and the mechanical brake will be adjusted to match driver's power request at the end of the prediction window while minimizing fuel consumption. The controller model is built on Matlab® MPC toolbox® and the simulations are based on MY04 Prius vehicle model using Autonomie®, a powertrain and fuel economy analysis software, developed by Argonne National Laboratory. The study compares the performance of MPC and conventional rule-base control methods. A parametric study is conducted to understand the influence of 3 weighting factors in MPC formulation on the performance of the vehicles. The conclusion provides a sound ground for further fuel consumption optimization and implementation of stochastic MPC.
SAE Technical Paper
Predictive control of a power-split HEV with fuel consumption and SOC estimation.
SAE Technical Paper.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/774