Xi-ming Chenga, Li-guang Yaoa, and Michael Pecht b
aCollaborative Innovation Center for Electric Vehicles in Beijing, National Engineering Laboratory for Electric Vehicles, Department of Vehicular Engineering, Beijing Institute of Technology, Beijing 100081, China
b CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
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.
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