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INTELLIGENT ENERGY MANAGEMENT AGENT FOR A PARALLEL HYBRID VEHICLE - PART I: SYSTEM ARCHITECTURE AND DESIGN OF THE DRIVING SITUATION IDENTIFICATION PROCESS

In this paper, the authors propose an intelligent energy management agent (IEMA) for parallel electric hybrid vehicles (HEVs). Using IEMA, the driving environment, driver’s driving style, and the vehicle’s operating mode are assessed. This information is then used by the torque distribution and charge sustenance components of IEMA, leading to enhanced fuel economy and reduced emissions. Part I presents the architecture of IEMA and describes the driving situation identification process. It is shown that a learning vector quantization (LVQ) network can, with a limited duration of driving data, effectively determine the driving condition.

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01005585
  • Record Type: Publication
  • Source Agency: UC Berkeley Transportation Library
  • Files: BTRIS, TRIS
  • Created Date: Oct 19 2005 2:03PM