Emerging lithium-ion battery systems require high-fidelity electrochemical models for advanced control, diagnostics, and design. Accordingly, battery parameter estimation is an active research domain where novel algorithms are being developed to calibrate complex models from input-output data. Amidst these efforts, little focus has been placed on the fundamental mechanisms governing estimation accuracy, spurring the question, why is an estimate accurate or inaccurate? In response, we derive a generalized estimation error equation under the commonly adopted least-squares objective function, which reveals that the error can be represented as a combination of system uncertainties (i.e., in model, measurement, and parameter) and uncertainty-propagating sensitivity structures in the data. We then relate the error equation to conventional error analysis criteria, such as the Fisher information matrix, Cramér-Rao bound, and parameter sensitivity, to assess the benefits and limitations of each. The error equation is validated through several uni- and bivariate estimations of lithium-ion battery electrochemical parameters using experimental data. These results are also analyzed with the error equation to study the error compositions and parameter identifiability under different data. Finally, we show that adding target parameters to the estimation without increasing the amount of data intrinsically reduces the robustness of the results to system uncertainties.
Comparison of parameter conditioning in output error and equation error approaches in speed and parameter estimation in induction machines
Equation error and output error are common formulations used in speed and parameter estimation for induction machines. This paper presents a study of the quality of the estimated speed and parameters using local sensitivity analysis. We studied parameter conditioning as a function of input signals and estimation methodology at nominal speed. Simulation results are used to show that output error formulation is better conditioned than equation error for speed and parameter estimation using PWM and six-step voltage inputs.
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- Award ID(s):
- 9702860
- NSF-PAR ID:
- 10317873
- Date Published:
- Journal Name:
- 2001 IEEE International Electric Machines and Drives Conference (IEMDC)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IEMDC.2001.939282
