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

Paper #:
  • 2017-01-1253

Published:
  • 2017-03-28
DOI:
  • 10.4271/2017-01-1253
Citation:
Sengupta, S., Gururaja, C., Hingane, S., K, P. et al., "Evaluation of Model Predictive and Conventional Method Based Hybrid Electric Vehicle Supervisory Controllers," SAE Technical Paper 2017-01-1253, 2017, doi:10.4271/2017-01-1253.
Pages:
12
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 different subsystems of HEV is an attractive proposition. In this paper, Model Predictive Control (MPC) and Equivalent Consumption Minimization Strategy (ECMS) based supervisory controllers have been developed to coordinate the power split between the two prime movers of an HEV – internal combustion engine and electric motor. A dynamical physics based HEV model has been developed for simulation of the system behavior. A cost function has been formulated to improve fuel economy and battery life. The dynamical structure of HEV along with its I/O, constraints, set points, operating points, etc. has been framed into the MPC controller that has been realized using Honeywell OnRAMP® Design Suite. Similarly, fuel and electricity consumption and efficiency models, and constraints have been framed into the ECMS controller. Results of the two controllers over standard drive cycles have been quantitatively compared against each other. Finally, qualitative analysis of the two approaches has been made in terms of their complexity, and tuning requirements for implementation.
Access
Now
SAE MOBILUS Subscriber? You may already have access.
Buy
Select
Price
List
Download
$27.00
Mail
$27.00
Members save up to 40% off list price.
Share
HTML for Linking to Page
Page URL

Related Items

Technical Paper / Journal Article
2004-03-08
Training / Education
2010-03-15
Training / Education
2005-07-01
Technical Paper / Journal Article
2003-06-17