A model-based state-of-charge estimation method for series-connected lithium-ion battery pack considering fast-varying cell temperature. (15th October 2019)

• Record Type: Journal Article
• Authors: Huang, Deyang; Chen, Ziqiang; Zheng, Changwen; Li, Haibin
• Is Part Of: Energy. Volume 185(2019)
• Page Start: 847
• Page End: 861
• Publication Date: 2019-10-15
• Subjects: Lithium-ion battery -- Inconsistency -- Model-based -- State-of-charge -- Fast-varying temperature; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.07.063
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Accurately estimating the state-of-charge (SOC) of lithium-ion batteries under complicated temperature conditions is crucial in all-climate battery management systems. This paper proposes a model-based SOC estimation method for series-connected battery pack with time-varying cell temperature. Systematic battery experiments are conducted to investigate the influences of changing temperature on both cell characteristics and cell-to-cell inconsistencies. A normalized open-circuit voltage (OCV) model is developed and applied in cell Thevenin model to describe the temperature-dependent OCV-SOC characteristic. The battery pack SOC is analyzed considering the effect of passive balance control. Then, a lumped parameter battery pack model is established by connecting cell models in series. To reduce computational complexity, a dual time-scale parameter identification framework is proposed which is supported by an online filtering process of selecting variable reference cell (VRC). An adaptive co-estimator is presented to update pack parameters in dual time-scale using an optimized recursive least squares algorithm, and to estimate the battery pack SOC using an extended Kalman filter. Experimental verifications are conducted under time-varying environmental temperature ranging from −40 °C to 40 °C. Results indicate the established model can well describe the dynamic behavior of battery pack, and the proposed method can estimate the battery pack SOC with considerably high precision. Highlights: Investigate cell-to-cell inconsistencies experimentally at different temperatures. Establish a temperature-dependent normalized OCV model. Select VRC online and develop a dual time-scale identification framework. Propose a VFFRLS-EKF based co-estimator considering fast-varying cell temperature. Prove the accuracy and robustness of proposed method experimentally.