Xi-ming Cheng1, Li-guang Yao1, and Michael Pecht2
1Collaborative Innovation Center for Electric Vehicles in Beijing, National Engineering Laboratory for Electric Vehicles, Department of Vehicular Engineering, Beijing Institute of Technology, Beijing 100081, China
2Center of Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
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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