Calculation vs. estimation: Impact of internal resistance determination on SOC estimation accuracy in lithium-ion batteries
Abstract
The electrical equivalent circuit model (ECM) is widely employed for state of charge (SOC) estimation in lithium-ion batteries. Among ECM-based approaches, the Thevenin equivalent circuit model (TECM) is particularly favored due to its computational efficiency and ease of parameter identification. TECM can be implemented in various configurations, with 1RC, 2RC, and 3RC structures being the most common. In these configurations, each RC unit consists of a resistor (R) and a capacitor (C) connected in parallel and incorporated into the circuit branch in series. As the number of RC branches increases, the computational burden of SOC estimation also rises. However, the improvement in estimation accuracy does not scale proportionally with the increased model complexity. A critical factor influencing the accuracy of SOC estimation is the precise determination of ECM parameters. A widely accepted principle suggests that when a parameter can be directly computed from the available data, it is preferable to use the calculated value rather than an estimated one. In line with this principle, this study directly calculates the internal resistance parameter (R0), which is connected in series with the RC branches in TECM, using test data while estimating the remaining RC parameters. SOC estimation is conducted for 1RC, 2RC, and 3RC configurations, and results are compared with those obtained from ECMs where all parameters, including R0, are estimated. To ensure a rigorous comparison, all estimations are performed using the nonlinear least squares method (LSM). The study employs test data from the US06 and UDDS driving cycles, and performance evaluation is conducted based on error distributions (box plots), root mean square error (RMSE), mean absolute error (MAE), and computational cost. The results showed that there is no significant difference between the error values of the predictions made using the model in which R0 is directly calculated and those made using the model in which R0 is estimated. Specifically, for the 1RC model structure, the lowest MAE values are 6.2 and 6.1 millivolts, respectively. However, these values indicate that the nonlinear LSM can be effectively used for estimating the parameters of the battery electrical circuit model.
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