A TD3-trained current excitation policy yields higher Fisher information and lower parameter estimation errors for anode and cathode rate constants than NMPC and conventional test profiles, in simulation only.
New data optimization framework for parameter estimation under uncertainties with application to lithium-ion battery,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
eess.SY 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning
A TD3-trained current excitation policy yields higher Fisher information and lower parameter estimation errors for anode and cathode rate constants than NMPC and conventional test profiles, in simulation only.