Robust Bayesian sequential input shaping for optimal Li-Ion battery model parameter identifiability

Michael J. Rothenberger, Hosam Kadry Fathy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper examines the challenge of shaping a battery's input trajectory to (i) maximize its Fisher parameter identifiability while (ii) achieving robustness to parameter uncertainties. The paper is motivated by earlier research showing that the speed and accuracy with which battery parameters can be estimated both improve significantly when battery inputs are optimized for Fisher identifiability. Previous research performs this trajectory optimization for a known nominal parameter set. This creates a tautology where accurate parameter identification is a prerequisite for Fisher identifiability optimization. In contrast, this paper presents an iterative scheme that: (i) uses prior parameter probability distributions to create a weighted Fisher metric; (ii) optimizes the battery input trajectory for this metric using a genetic algorithm; (iii) applies the resulting input trajectory to the battery; (iv) estimates battery parameters using a Bayesian particle filter; (v) re-computes the weighted Fisher information metric using the resulting posterior parameter distribution; and (vi) repeats this process until convergence. This approach builds on well-established ideas from the estimation literature, and applies them to the battery domain for the first time. Simulation studies highlight the ability of this iterative algorithm to converge quickly towards the correct battery parameter values, despite large initial parameter uncertainties.

Original languageEnglish (US)
Title of host publicationDiagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791857250
DOIs
StatePublished - Jan 1 2015
EventASME 2015 Dynamic Systems and Control Conference, DSCC 2015 - Columbus, United States
Duration: Oct 28 2015Oct 30 2015

Publication series

NameASME 2015 Dynamic Systems and Control Conference, DSCC 2015
Volume2

Other

OtherASME 2015 Dynamic Systems and Control Conference, DSCC 2015
CountryUnited States
CityColumbus
Period10/28/1510/30/15

Fingerprint

Trajectories
Probability distributions
Identification (control systems)
Genetic algorithms
Lithium-ion batteries
Uncertainty

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Mechanical Engineering
  • Control and Systems Engineering

Cite this

Rothenberger, M. J., & Fathy, H. K. (2015). Robust Bayesian sequential input shaping for optimal Li-Ion battery model parameter identifiability. In Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications (ASME 2015 Dynamic Systems and Control Conference, DSCC 2015; Vol. 2). American Society of Mechanical Engineers. https://doi.org/10.1115/DSCC2015-9942
Rothenberger, Michael J. ; Fathy, Hosam Kadry. / Robust Bayesian sequential input shaping for optimal Li-Ion battery model parameter identifiability. Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications. American Society of Mechanical Engineers, 2015. (ASME 2015 Dynamic Systems and Control Conference, DSCC 2015).
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Rothenberger, MJ & Fathy, HK 2015, Robust Bayesian sequential input shaping for optimal Li-Ion battery model parameter identifiability. in Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications. ASME 2015 Dynamic Systems and Control Conference, DSCC 2015, vol. 2, American Society of Mechanical Engineers, ASME 2015 Dynamic Systems and Control Conference, DSCC 2015, Columbus, United States, 10/28/15. https://doi.org/10.1115/DSCC2015-9942

Robust Bayesian sequential input shaping for optimal Li-Ion battery model parameter identifiability. / Rothenberger, Michael J.; Fathy, Hosam Kadry.

Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications. American Society of Mechanical Engineers, 2015. (ASME 2015 Dynamic Systems and Control Conference, DSCC 2015; Vol. 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - This paper examines the challenge of shaping a battery's input trajectory to (i) maximize its Fisher parameter identifiability while (ii) achieving robustness to parameter uncertainties. The paper is motivated by earlier research showing that the speed and accuracy with which battery parameters can be estimated both improve significantly when battery inputs are optimized for Fisher identifiability. Previous research performs this trajectory optimization for a known nominal parameter set. This creates a tautology where accurate parameter identification is a prerequisite for Fisher identifiability optimization. In contrast, this paper presents an iterative scheme that: (i) uses prior parameter probability distributions to create a weighted Fisher metric; (ii) optimizes the battery input trajectory for this metric using a genetic algorithm; (iii) applies the resulting input trajectory to the battery; (iv) estimates battery parameters using a Bayesian particle filter; (v) re-computes the weighted Fisher information metric using the resulting posterior parameter distribution; and (vi) repeats this process until convergence. This approach builds on well-established ideas from the estimation literature, and applies them to the battery domain for the first time. Simulation studies highlight the ability of this iterative algorithm to converge quickly towards the correct battery parameter values, despite large initial parameter uncertainties.

AB - This paper examines the challenge of shaping a battery's input trajectory to (i) maximize its Fisher parameter identifiability while (ii) achieving robustness to parameter uncertainties. The paper is motivated by earlier research showing that the speed and accuracy with which battery parameters can be estimated both improve significantly when battery inputs are optimized for Fisher identifiability. Previous research performs this trajectory optimization for a known nominal parameter set. This creates a tautology where accurate parameter identification is a prerequisite for Fisher identifiability optimization. In contrast, this paper presents an iterative scheme that: (i) uses prior parameter probability distributions to create a weighted Fisher metric; (ii) optimizes the battery input trajectory for this metric using a genetic algorithm; (iii) applies the resulting input trajectory to the battery; (iv) estimates battery parameters using a Bayesian particle filter; (v) re-computes the weighted Fisher information metric using the resulting posterior parameter distribution; and (vi) repeats this process until convergence. This approach builds on well-established ideas from the estimation literature, and applies them to the battery domain for the first time. Simulation studies highlight the ability of this iterative algorithm to converge quickly towards the correct battery parameter values, despite large initial parameter uncertainties.

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Rothenberger MJ, Fathy HK. Robust Bayesian sequential input shaping for optimal Li-Ion battery model parameter identifiability. In Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications. American Society of Mechanical Engineers. 2015. (ASME 2015 Dynamic Systems and Control Conference, DSCC 2015). https://doi.org/10.1115/DSCC2015-9942