NONLINEAR OBSERVERS FOR PREDICTING STATE-OF-CHARGE AND STATE-OF-HEALTH OF LEAD-ACID BATTERIES FOR HYBRID ELECTRIC VEHICLES

This paper presents an alternative approach that uses a Kalman Filter for estimating the state-of-charge (SoC) and state-of-health (SoH) of a lead-acid battery cell pack. Large state errors can develop over time when using a generic model to describe the dynamic behavior of lead-acid cells. A Kalman Filter, with its inherent predictor-corrector mechanism, can accommodate these types of errors. Measurements using real-time road data collected from a Honda Insight HEV driven on a test track are used in comparing the results derived from a conventional model with those from the Kalman Filter state estimation methods. Results show not only significant improvements in SoC estimation, but they also demonstrate the ability of a battery to forecast the response of a cell to driving demands, thus leading to optimal utilization of the battery pack and better energy management within the vehicle.

Language

  • English

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Filing Info

  • Accession Number: 01005576
  • Record Type: Publication
  • Source Agency: UC Berkeley Transportation Library
  • Files: BTRIS, TRIS
  • Created Date: Oct 19 2005 2:02PM