Fuel economy in a hybrid electric vehicle is significantly affected by the battery discharge strategy. This paper investigates the blended battery discharge strategy with four possible battery State of Charge (SOC) profiles to compare the fuel savings possible over a default Charge Discharge – Charge Sustaining strategy, given that the vehicle’s duty cycle is known. A pickup & delivery truck with Range Extended Electric Vehicle (REEV) powertrain architecture has been modeled. Vehicle speed control is implemented using a distance-based driver that matches the distance traveled from every start to stop in the drivecycle. On-board energy management is implemented using the Energy Consumption Minimization Strategy (ECMS). It is found that a predicted power consumption-based battery discharge profile results in the least fuel consumption. A distance based discharge has relatively higher fuel consumption but is quite close.
Skip Nav Destination
ASME 2018 Dynamic Systems and Control Conference
September 30–October 3, 2018
Atlanta, Georgia, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-5189-0
PROCEEDINGS PAPER
Battery Discharge Strategies for Energy Management in Electrified Truck for Pickup and Delivery Application
Mukilan T. Arasu,
Mukilan T. Arasu
Ohio State University, Columbus, OH
Search for other works by this author on:
Qadeer Ahmed,
Qadeer Ahmed
Ohio State University, Columbus, OH
Search for other works by this author on:
Giorgio Rizzoni
Giorgio Rizzoni
Ohio State University, Columbus, OH
Search for other works by this author on:
Mukilan T. Arasu
Ohio State University, Columbus, OH
Qadeer Ahmed
Ohio State University, Columbus, OH
Giorgio Rizzoni
Ohio State University, Columbus, OH
Paper No:
DSCC2018-9116, V001T09A004; 10 pages
Published Online:
November 12, 2018
Citation
Arasu, MT, Ahmed, Q, & Rizzoni, G. "Battery Discharge Strategies for Energy Management in Electrified Truck for Pickup and Delivery Application." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare. Atlanta, Georgia, USA. September 30–October 3, 2018. V001T09A004. ASME. https://doi.org/10.1115/DSCC2018-9116
Download citation file:
24
Views
Related Proceedings Papers
Related Articles
Model-Based Fuel Optimal Control of Hybrid Electric Vehicle Using Variable Structure Control Systems
J. Dyn. Sys., Meas., Control (January,2007)
A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles
J. Dyn. Sys., Meas., Control (May,2011)
An Intelligent Predictive Controller for Power and Battery Management in Plug-In Hybrid Electric Vehicles
J. Energy Resour. Technol (November,2021)
Related Chapters
Research on Energy Management Strategy of Fuel Cell/Battery Hybrid Electric Automobile
International Conference on Mechanical and Electrical Technology 2009 (ICMET 2009)
Introduction
Ultrasonic Welding of Lithium-Ion Batteries
Concluding Remarks and Future Work
Ultrasonic Welding of Lithium-Ion Batteries