Excitation Optimization for Estimating Battery Health Parameters using Reinforcement Learning considering Information Content and Bias
Author:
Affiliation:
1. University of California,Department of Mechanical and Aerospace Engineering,Davis,CA,USA,95616
2. The University of Sheffield,Department of Automatic Control and Systems Engineering,Sheffield,UK
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10155646/10155787/10155899.pdf?arnumber=10155899
Reference20 articles.
1. Input Excitation Optimization for Estimating Battery Electrochemical Parameters using Reinforcement Learning
2. Data Optimization for Parameter Estimation under System Uncertainties with Application to Li-ion Battery
3. Extensions of the Dynamic Programming Framework: Battery Scheduling, Demand Charges, and Renewable Integration
4. Reinforcement Learning of Optimal Input Excitation for Parameter Estimation With Application to Li-Ion Battery
5. Enabling high-fidelity electrochemical P2D modeling of lithium-ion batteries via fast and non-destructive parameter identification
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