TY - GEN
T1 - Sensitivity Analysis of Battery Digital Twin Design Variables Using Genetic Programming
AU - Vandana,
AU - Bose, Bibaswan
AU - Garg, Akhil
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - feature extraction
KW - Genetic Programming
KW - Sensitivity Analysis
KW - State Estimation
KW - Structural Risk Minimization
UR - http://www.scopus.com/inward/record.url?scp=85173608075&partnerID=8YFLogxK
U2 - 10.1109/SeFeT57834.2023.10244776
DO - 10.1109/SeFeT57834.2023.10244776
M3 - Conference Proceeding
AN - SCOPUS:85173608075
T3 - 2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation, SeFet 2023
BT - 2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation, SeFet 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Sustainable Energy and Future Electric Transportation, SeFet 2023
Y2 - 9 August 2023 through 12 August 2023
ER -
