Genetic Algorithm Driven Generic Estimation Model of Lithium-Ion Battery for Energy Balance Calculation in Spacecraft

• Published in: IEEE transactions on industry applications Vol. 57; no. 3; pp. 2726 - 2736
• Main Authors: S., Ananda, Lakshminarasamma, N., V., Radhakrishna, S., Srinivasan M., P., Satyanarayana, M., Sankaran
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batteries; Data models; Energy balance; Energy storage; genetic algorithm (GA); Genetic algorithms; Integrated circuit modeling; Lithium; Lithium-ion batteries; lithium-ion battery model; Load modeling; Mathematical model; Open circuit voltage; Parameter estimation; Rechargeable batteries; Space vehicles; Spacecraft
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2021.3059194

Lithium-ion battery is generally used as energy storage device in spacecraft and other applications. Spacecraftbattery modeling and energy balance estimation is critical. The proposed genetic algorithm driven generic estimation - energy balance (GAGE-EB) model uses cell open circuit voltage characteristics, internal resistance (<inline-formula><tex-math notation="LaTeX">r_{\text{int}}</tex-math></inline-formula>) characteristics, and coulomb counting. The cell characteristics are obtained by offline tests. For series-parallel or parallel-series configured battery, the model estimates deliverable capacity and internal resistance by accurately matching the terminal voltage. Estimated parameters are used to check the energy balance for new load and generation profiles. The GAGE-EB model is validated for electrical performance with various cell types and battery configurations with respect to experimental data and is within 1% and 2% accuracy for voltage and capacity estimation, respectively.