Data-based modeling of a lithium iron phosphate battery as an energy storage and delivery system

• Published in: 2013 American Control Conference pp. 1908 - 1913
• Main Authors: Xin Zhao, de Callafon, Raymond A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2013
• Subjects: Batteries; Current measurement; Discharges (electric); Integrated circuit modeling; Predictive models; Voltage measurement
• ISBN: 1479901776; 9781479901777
• ISSN: 0743-1619
• DOI: 10.1109/ACC.2013.6580114

Lithium-ion batteries are important for storage and delivery of electrical energy. Monitoring and prediction of the dynamic and time-dependent effects of lithium-ion batteries is crucial in a battery management system (BMS). In this paper, a dynamic model for the battery as an energy storage and delivery system is proposed. The structure and the parameters of the battery models are estimated by monitoring a charge/discharge demand signal and a power storage/delivery signal in real time. The model is combined by individual linear dynamic models, where the parameters can be estimated by a least-squares algorithm and implemented in a recursive fashion. Based on data obtained from the experimental setup, the dynamic model is applied to predict the dynamics of the energy storage and delivery, and validated against real-time measurements. The results show that the model can capture and predict the dynamics of the energy storage and delivery of the battery, which can benefit the control of lithium-ion batteries.