摘要:Accurate estimation of the state of charge (SOC) of batteries is an essential task of the battery
management system (BMS). The effectiveness of the adaptive extended Kalman filter (AEKF)
model-based observer for SOC estimation of the dynamic behavior of the battery is investigated.
The SOC is a reflexion of the chemistry of the cell; it is the key parameter for the BMS. In this
paper, three equivalent circuits models (ECMs) have been established and their parameters were
identified by applying the least square method. However, the relationship between open circuit
voltage (OCV) and SOC have been proposed by four mathematical functions model-based. In
fact, the SOC estimation accuracy of the battery depends on the model and the efficiency of the
algorithm. The AEKF method is used to estimate the SOC of Lead acid battery. The
experimental data is employed to identify the parameters of the three models and used to build
different open circuit voltage—state of charge (OCV-SOC) functions relationship. The results
show that the SOC estimation based-model on high order polynomial and third-order equivalent
circuit can effectively limit the error, thus, guaranteeing the accuracy and robustness.
关键词:Adaptive Kalman filter; state of charge estimation; open circuit voltage; lead-acid;
batteries; least square method.