A Novel Adaptive SOC Estimation Method for a Series-connectedLithium-ion Battery Pack Under Fast-varying Environment Temperature

Deyang Huang, Ziqiang Chen, Changwen Zheng


This paper proposes a model-based state-of-charge (SOC) estimation method for a series-connected battery pack under fast-varying environment temperature. For accurately evaluating the SOC of the battery pack equipped with passive balance control, the relationship of battery pack and in-pack cell in the available capacity is analyzed. A filtering process is applied to select the alterable reference cell (ARC) for supporting the modeling framework of SOC estimation. An adaptive SOC estimator is presented by using an optimized recursive least squares algorithm to identify all cells parameters, and using an adaptive extended Kalman filter algorithm (AEKF) to estimate the pack generalized SOC in real-time. Furthermore, a bias correction approach is developed to compensate the cell available capacity at low temperature based on the cell resistance of off-line identification and the open-circuit voltage (OCV) of on-line estimation. The experimental verification is conducted through the modified Federal Urban Driving Schedule (FUDS) cycles with environment temperature varying from 25°C to -35°C. Results show that the accuracy of the proposed SOC estimation method is considerably high, and the correction approach can compensate the cell available capacity effectively with acceptable error.


lithium-ion battery pack, state-of-charge estimation, fast-varying environment temperature, passive balance control


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