Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting

• Published in: Proceedings of the 2011 American Control Conference pp. 935 - 940
• Main Authors: Hu, Xiaosong, Sun, Fengchun, Zou, Yuan, Peng, Huei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2011
• Subjects: Batteries; Battery charge measurement; Integrated circuit modeling; Mathematical model; System-on-a-chip; Transient analysis; Voltage measurement
• ISBN: 1457700808; 9781457700804
• ISSN: 0743-1619
• DOI: 10.1109/ACC.2011.5991260

A battery model that is suitable for real-time State-of-Charge (SOC) estimation of a Lithium-Ion battery is presented in this paper. The battery open circuit voltage (OCV) as a function of SOC is described by an adaptation of the Nernst equation. The analytical representation can facilitate Kalman filtering or observer-based SOC estimation methods. A zero-state hysteresis correction term is used to depict the hysteresis effect of the battery. A parallel resistance-capacitance (RC) network is used to depict the relaxation effect of the battery. A linear discrete-time formulation of the battery model is derived. A recursive least squares algorithm with forgetting is applied to implement the online parameter calibration. Validation results show that the calibrated model can accurately simulate the dynamic voltage behavior of the Lithium-Ion battery for two different experimental data sets.