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Conditional Extended Kalman Filter for Battery Model Parameter Identification

[+] Author Affiliations
Yonghua Li, Xu Wang

Ford Motor Company, Dearborn, MI

Paper No. DSCC2014-5820, pp. V002T36A001; 7 pages
  • ASME 2014 Dynamic Systems and Control Conference
  • Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing
  • San Antonio, Texas, USA, October 22–24, 2014
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4619-3
  • Copyright © 2014 by ASME


In this paper a conditional Extended Kalman Filter is applied to battery model parameter and state estimations. A decision logic, based on battery input and output data, is designed such that parameter update is stopped when persistent excitation conditions are not met. Persistent excitation conditions are represented by a simpler, easier to implement set of calibrations. Examples, both from desktop simulation, and real world vehicle testing, have been provided to support the validity of this algorithm. The proposed strategy has been successfully deployed in production FHEV/PHEV vehicles.

Copyright © 2014 by ASME



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