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Real-Time Battery Model Parameter Estimation With Improved Observability and Robustness

[+] Author Affiliations
Tae-Kyung Lee, Dyche Anderson

Ford Motor Company, Dearborn, MI

Paper No. DSCC2014-5825, pp. V002T36A002; 5 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


Prediction of battery system responses and capability for next few seconds can provide key information to use battery hardware effectively. The prediction performance will be much improved, when battery models can capture the real battery responses as accurate as possible. Equivalent circuit models (ECMs) have been used for control purpose due to their proper balance between computational efficiency and prediction accuracy. The limitations of ECMs can be efficiently compensated through real-time model parameter estimation. Further enhancement is possible by improving system observability and robustness, specifically effective under low temperature and aggressive driving. This paper proposes an approach to improve the robustness and accuracy in estimating parameters by reformulating ECMs with new parameters. The proposed approach can estimate battery parameters less sensitive to both external disturbance and possible model mismatch under various driving conditions.

Copyright © 2014 by ASME



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