Abstract
An effective model of battery performance is important for battery management systems to control the state of battery and cell balancing. The second-order equivalent circuit model of a lithium-ion battery is studied in the present paper. The identification methods that include the multiple linear regression (MLR), exponential curve fitting (ECF) and Simulink design optimization tool (SDOT), were used to determine the model parameters. The aim of this paper is to compare the validity of the three proposed algorithms, which vary in complexity. The open circuit voltage was measured based on the pulse discharge test. The voltage response was collected for every 10% SOC in the interval between 0–100% SOC. The battery voltages calculated from the estimated parameters under the constant current discharge test and dynamic discharge tests for electric vehicles (ISO and WLTP) were compared to the experimental data. The mean absolute error and root mean square error were calculated to analyze the accuracy of the three proposed estimators. Overall, SDOT provides the best fit with high accuracy, but requires a heavy computation burden. The accuracy of the three methods under the constant current discharge test is high compared to other experiments, due to the nonlinear behavior at a low SOC. For the ISO and WLTP dynamic tests, the errors of MLR are close to that of SDOT, but have less computing time. Therefore, MLR is probably more suitable for EV use than SDOT.
Highlights
In recent years, there has been growing interest in energy storage technology due to the power application in portable devices, photovoltaic storage [1], industrial devices [2] and electric vehicles (EVs) [3–5]
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It is found that the mean absolute error (MAE) of three estimators under the constant current (CC) discharge test is comparable
Summary
There has been growing interest in energy storage technology due to the power application in portable devices, photovoltaic storage [1], industrial devices [2] and electric vehicles (EVs) [3–5]. The three key issues that were intensively studied are (i) battery performance testing, (ii) battery model and (iii) BMS technology. These three relevant factors work together: battery parameters measured from battery tests are put into the effective battery model in order to achieve a practical battery technology for battery management. Compared with the non-model-based approach, the model-based method is widely used for EV application. The DD methods can be used to estimate the SOC by measuring the input parameters (current, voltage and temperature) [17,18]. It was found that the ANN and GPR algorithms achieved good performance in the SOC estimation of an Li-ion battery. The changes of the parameters as a function of age are in a good agreement with the expected trends
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