A Dynamic SOH-coupled Lithium-ion Cell Model for State and Parameter Estimation

• Published in: IEEE transactions on energy conversion Vol. 38; no. 2; pp. 1 - 10
• Main Authors: Vennam, Geetika, Sahoo, Avimanyu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aging; Batteries; Battery Management System (BMS); Core and Surface Temperature; Dependent variables; Electric cells; Electric charge; Equivalent circuits; Estimation; Graphite; Lithium-ion batteries; Lithium-ion battery; Management systems; Mathematical models; Numerical models; Parameter estimation; Power management; Rechargeable batteries; Resistance; Simultaneous State and Parameter Estimation; State of charge; State of charge (SOC); State of health (SOH); Temperature; Thermal analysis
• ISSN: 0885-8969; 1558-0059
• DOI: 10.1109/TEC.2022.3218344

The health assessment of Lithium-ion batteries (LIBs) is critical for battery management systems (BMSs) to ensure safe and reliable operation and predict life-cycle. State-of-health (SOH) monitoring is challenging since it is governed by several internal and external degradation factors, such as temperature, aging, <inline-formula><tex-math notation="LaTeX">C_{rate}</tex-math></inline-formula>, and faults. In this paper, we propose a SOH-coupled nonlinear electro-thermal-aging (ETA) model of a <inline-formula><tex-math notation="LaTeX">LiFePO_{4}</tex-math></inline-formula>/graphite battery, which can be employed to simultaneously estimate the state of charge (SOC), state of health (SOH), temperatures, and internal resistance using a filtering-based approach. The coupling between the equivalent circuit model (ECM) and the SOH is established using an empirical capacity fade model of a <inline-formula><tex-math notation="LaTeX">LiFePO_{4}</tex-math></inline-formula>/graphite battery and its effects on SOC dynamics. In contrast to a constant usable capacity, the proposed model employs a SOH-dependent variable capacity ECM, thereby incorporating the influence of battery aging on the ECM. The SOH-coupled ECM model is then integrated with the thermal model to develop the ETA model. The ETA model is further extended by augmenting the ohmic resistance dynamics to enable monitoring of the evolution of the internal resistance. The proposed SOH-coupled model is validated with numerical simulation and experimental data. Estimation results for SOC, SOH, temperature and ohmic resistance are included to show the model's potential for monitoring and control applications.