Intelligent energy management agent for a parallel hybrid vehicle - Part 1: System architecture and design of the driving situation identification process Academic Article

abstract

• This two part paper proposes an intelligent energy management agent (IEMA) for parallel hybrid vehicles. IEMA incorporates a driving situation identification component whose role is to assess the driving environment, the driving style of the driver and the operating mode of the vehicle using long and short term statistical features of the drive cycle. This information is subsequently used by the torque distribution and charge sustenance components of IEMA to determine the power split strategy, which is shown to lead to enhanced fuel economy and reduced emissions. In Part I, the overall architecture of IEMA is presented and the driving situation identification process is described. It is specifically shown that a learning vector quantization (LVQ) network can effectively determine the driving condition using a limited duration of driving data. The overall performance of the system under a range of drive cycles is discussed in the second part of this paper. 2005 IEEE.

authors

• Langari, Gholamreza

published proceedings

• IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY

altmetric score

• 6

author list (cited authors)

• Langari, R., & Won, J. S.

citation count

• 222

complete list of authors

• Langari, R||Won, JS

publication date

• May 2005

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Vehicular Technology  Journal

keywords

• Charge Sustenance
• Drive Cycle
• Energy Management
• Fuzzy Rule Base
• Hybrid Vehicle
• Torque Distribution

Digital Object Identifier (DOI)

• 10.1109/TVT.2005.844685

start page

• 925

end page

• 934

volume

• 54

issue

• 3

URL

• http://dx.doi.org/10.1109/tvt.2005.844685

user-defined tag

• 7 Affordable and Clean Energy