Intelligent Energy Distribution for Series HEVs Using Determined Optimal Driving Patterns via a Genetic Algorithm North Carolina State University

Format:

Conference/Event

Author/Creator:

He, He, author.

Contributor:

Klang, Eric

Conference Name:

SAE 2013 World Congress & Exhibition (2013-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2013

Summary:

This paper introduces an intelligent energy distribution scheme for series plug-in hybrid electric vehicles (PHEVs) which incorporates the complexity of human driving behavior. Driving styles can have a significant impact on fuel consumption, but it is often unclear how one should drive to get the optimal fuel efficiency. Hybrid electric vehicles have been shown to improve fuel economy, reduce vehicle emissions and maintain drivability by incorporating electric motors into the drivetrain. Due to the highly complex system design and vehicle architecture, sophisticated energy management strategies (EMS) are required to optimize the vehicle performance. Currently, the power management system is based on static thresholds optimized on a fixed drive cycle for a given vehicle. This paper introduces an adaptive control method for EMS utilizing the complexity of human driving patterns for energy distribution in a series PHEV. By utilizing the Genetic Algorithm (GA) optimization method to determine the optimal driving patterns offline, an optimal EMS can be derived to maintain an effective control algorithm in real-time

Notes:

Vendor supplied data

Publisher Number:

2013-01-0572

Access Restriction:

Restricted for use by site license