Reinforcement Learning of Optimal Input Excitation for Parameter Estimation With Application to Li-Ion Battery
Author:
Affiliation:
1. Department of Mechanical and Aerospace Engineering, University of California, Davis, CA, USA
Funder
NSF CAREER
NASA HOME Space Technology Research Institute
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/9424/10255543/10043018.pdf?arnumber=10043018
Reference35 articles.
1. New data optimization framework for parameter estimation under uncertainties with application to lithium-ion battery
2. System Identification of Battery Single Particle Model Parameters Using New Data Optimization Approach
3. Analytic Bound on Accuracy of Battery State and Parameter Estimation
4. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models
5. On a Measure of the Information Provided by an Experiment
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2. Error Analysis for Parameter Estimation of Li-ion Battery subject to System Uncertainties;2023 American Control Conference (ACC);2023-05-31
3. Excitation Optimization for Estimating Battery Health Parameters using Reinforcement Learning considering Information Content and Bias;2023 American Control Conference (ACC);2023-05-31
4. A Non-dimensional Input Excitation Optimization Approach for Battery Health Parameter Estimation;IFAC-PapersOnLine;2023
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