Battery Power Management in Heavy-duty HEVs based on the Estimated Critical Surface Charge

Abstract

Real time battery performance in a Hybrid Electric Vehicle (HEV) is significantly affected by the battery allowable power limits. This is particularly true in the case of large vehicles, where rates of energy flows through the system reach up to the marginal values during aggressive acceleration or braking. The underlying phenomenon determining the limits is closely connected to the critical surface charge (CSC) defined by the average positive electrode concentration at the solid particle surface in the cell. This paper characterizes the CSC under high discharging power with respect to the initial battery state of charge (SOC), and subsequently utilizes the insight to propose a novel approach to design supervisory control of a series HEV. The new strategy includes a battery power management logic that prevents battery over-charging and overdischarging under aggressive driving conditions. The CSC estimated by the extended Kalman filter (EKF) is processed with a finite impulse response (FIR) filter to smooth out short-term fluctuations and highlight longer-term trajectories. Then, the filtered CSC sequence is used to determine the battery allowable power limits in real time and feedbacked to the supervisory controller. The proposed strategy is implemented in the heavy-duty HEV simulation framework and its effectiveness is validated under an aggressive real-world military cycle. Undesirable battery operations and potential possibility of the complete Lithium-ion depletion are prevented, thus improving battery health prospects without any penalty on fuel efficiency.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2011
Accession Number
ADA576964

Entities

People

  • Denise Kramer
  • Tae-kyung Lee
  • Youngki Kim
  • Zoran Filipi

Organizations

  • University of Michigan

Tags

DTIC Thesaurus Topics

  • Efficiency
  • Electric Motors
  • Electric Vehicles
  • Electrodes
  • Energy
  • Energy Management
  • Energy Storage
  • Filters
  • Fuel Consumption
  • Fuel Efficiency
  • Heavy Duty
  • Hybrid Electric Vehicles
  • Kalman Filters
  • Lithium Ion Batteries
  • Simulations
  • Supervisory Control
  • Vehicles

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Battery Technology and Engineering
  • Economics