Evaluation of Model Predictive and Conventional Method Based Hybrid Electric Vehicle Supervisory Controllers

Paper #:
  • 2017-01-1253

Published:
  • 2017-03-28
Abstract:
Increasingly strict CO2 and emissions norms are pushing the automotive industry towards increasing adoption of Hybrid Electric Vehicle (HEV) technology. HEVs are complex hardware systems which are often controlled by software that is complex to maintain, time-consuming to calibrate, and not always guaranteed to deliver optimal fuel economy. Hence, coordinated, systematic control of the HEVs different subsystems is an attractive proposition. In this paper, Model Predictive Control (MPC) based supervisory controller is developed to coordinate the power split between the two prime movers of an HEV – internal combustion engine and electric motor. A cost function has been formulated to improve fuel economy and battery life. A dynamical physics based HEV model has been developed for simulation of the system behavior. The dynamical structure of HEV along with its I/O, constraints, set points, operating points, etc. has been framed into the MPC controller. The MPC solution has been realized using Honeywell OnRAMP Design Suite. Additionally, for the same HEV system, a supervisory controller based on conventionally used Equivalent Consumption Minimization Strategy (ECMS) is developed and its corresponding results over standard drive cycles are quantitatively compared against those for MPC. Finally, qualitative analysis of the two approaches is made in terms of their complexity, memory, tuning and validation requirements for implementation.
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