Intelligent Energy Management Agent for a Parallel Hybrid Vehicle—Part I: System Architecture and Design of the Driving Situation Identification Process

TLDR

The paper introduces an intelligent energy management agent (IEMA) for parallel hybrid vehicles, with Part I detailing its overall architecture and the driving‑situation identification process. IEMA incorporates a driving‑situation identification component that extracts long‑ and short‑term statistical features from the drive cycle, uses a learning vector quantization network to classify driving conditions, and feeds this information to torque‑distribution and charge‑sustenance modules to decide the power‑split strategy. The study demonstrates that the LVQ network can accurately determine driving conditions from short data segments, and that IEMA improves fuel economy while reducing emissions.

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.