TY - GEN
T1 - Optimal Parameter Estimation of CAPN Model for Li-ion Battery
AU - Bharti, Soumya
AU - Saini, Vikash Kumar
AU - Kumar Yadav, Anshul
AU - Kumar, Rajesh
AU - Al-Sumaiti, Ameena S.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Recent developments in battery technology and the reduction of battery costs have resulted in battery energy storage systems (BESS) becoming more economically viable for various power sectors and automobile applications. The degradation of BESS when used in conjunction with photovoltaics (PV) and electric vehicles (EVs) brings new challenges in both technical and economic analysis. Consequently, to ensure the reliability and safety of an energy storage system, modeling battery degradation is critical in order to maintain and replace batteries before potential issues arise. This paper presents a hybrid optimal parameter estimation technique for a combination-factor-based semi-empirical approach to evaluate the state of health (SOH) of Li-ion batteries. The parameters of the proposed model are determined using the SCA optimization algorithm, and the numerical results are confirmed by placing them side-by-side with other heuristic algorithms such as GWO, DE, HHO, and GA. The proposed model outperformed the traditional model with a SOH value of 0.9591 as compared to the previous SOH obtained 0.9467 by the traditional model.
AB - Recent developments in battery technology and the reduction of battery costs have resulted in battery energy storage systems (BESS) becoming more economically viable for various power sectors and automobile applications. The degradation of BESS when used in conjunction with photovoltaics (PV) and electric vehicles (EVs) brings new challenges in both technical and economic analysis. Consequently, to ensure the reliability and safety of an energy storage system, modeling battery degradation is critical in order to maintain and replace batteries before potential issues arise. This paper presents a hybrid optimal parameter estimation technique for a combination-factor-based semi-empirical approach to evaluate the state of health (SOH) of Li-ion batteries. The parameters of the proposed model are determined using the SCA optimization algorithm, and the numerical results are confirmed by placing them side-by-side with other heuristic algorithms such as GWO, DE, HHO, and GA. The proposed model outperformed the traditional model with a SOH value of 0.9591 as compared to the previous SOH obtained 0.9467 by the traditional model.
KW - Battery degradation model
KW - Li-ion battery
KW - Optimization algorithm
UR - http://www.scopus.com/inward/record.url?scp=85174496984&partnerID=8YFLogxK
U2 - 10.1109/IC2E357697.2023.10262727
DO - 10.1109/IC2E357697.2023.10262727
M3 - Conference contribution
AN - SCOPUS:85174496984
T3 - 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
BT - 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
Y2 - 8 June 2023 through 9 June 2023
ER -
