Sensitivity Analysis of Battery Digital Twin Design Variables Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Vandana:2023:SEFET,
-
author = "Vandana and Bibaswan Bose and Akhil Garg",
-
booktitle = "2023 IEEE 3rd International Conference on Sustainable
Energy and Future Electric Transportation (SEFET)",
-
title = "Sensitivity Analysis of Battery Digital Twin Design
Variables Using Genetic Programming",
-
year = "2023",
-
abstract = "The advancement of digital twin (DT) technology
improves battery performance and lifespan. Although
precise forecasting, selection of design variables, and
risk reduction are challenging. Therefore, it is
critical in implementation of practical DT to
investigate the sensitivity of feature implications on
state estimation thoroughly. Hence in this paper, an
analysis of features has been piloted using voltage and
current characteristics. First, features have been
extracted from performance values. Secondly, genetic
programming (GP) has been set up to reflect the impact
on state estimations. Structural risk minimization is
used as a fitness function to maximize the DT's
objective function, while GP-battery state estimation
is implemented. An illustrative example is presented to
evaluate the state of experimental data generated in
the lab under controlled environmental conditions.
Based on the analysis, the state of charge shows
precision incorporation of all features, while the
change in current over voltage shows the improvement in
state of energy estimation. State of power is more
sensitive towards changes in voltage concerning changes
in current, and state of health offers better accuracy
to the present voltage over the current applied. A
sensitivity rating has been compared to design the role
of the feature variable.",
-
keywords = "genetic algorithms, genetic programming,
Transportation, Voltage, Feature extraction, Linear
programming, Batteries, Digital twins, Structural Risk
Minimization, feature extraction, State Estimation,
Sensitivity Analysis",
-
DOI = "doi:10.1109/SeFeT57834.2023.10244776",
-
month = aug,
-
notes = "Also known as \cite{10244776}
Centre for Automotive Research and Tribology, Indian
Institute of Technology,Delhi, India",
- }
Genetic Programming entries for
Vandana
Bibaswan Bose
Akhil Garg
Citations