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Abstract: Equivalent circuit model-based state-of-charge (SOC) estimation has been widely studied for power lithium-ion batteries. An appropriate relaxation period to measure the open-circuit voltage (OCV) should be investigated to both ensure good SOC estimation accuracy and improve OCV test efficiency. Based on a battery circuit model, an SOC estimator in the combination of recursive least squares (RLS) and the extended Kalman filter is used to mitigate the error voltage between the measurement and real values of the battery OCV. To reduce the iterative computation complexity, a two-stage RLS approach is developed to identify the model parameters, the battery circuit of which is divided into two simple circuits. Then, the measurement values of the OCV at varying relaxation periods and three temperatures are sampled to establish the relationships between SOC and OCV for the developed SOC estimator. Lastly, dynamic stress test and federal test procedure drive cycles are used to validate the model-based SOC estimation method. Results show that the relationships between SOC and OCV at a short relaxation time, such as 5 min, can also drive the SOC estimator to produce a good performance.

This paper shows original work towards development of a two stage process to estimate the EC model parameters and determine battery SOC. The circuit deconstruction method was validated with DST and FTP test cycles. The uniqueness of the work lies in the reduction in error in estimation of SOC and also reducing in the OCV test time. The authors have followed a reasonable scientific approach to explain the model development, theory and results.

不同开路电压松弛时间下基于等效电路解构的锂离子电池荷电状态估计

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