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Various methods have been proposed to estimate the capacity of lithium-ion batteries through constant current constant voltage charging. Existing algorithms require limiting the charging current and starting the charge from a specific low state of charge (SOC). In this paper, a capacity estimation algorithm for various initial SOC and 2 C charging currents is proposed. The proposed algorithm estimates capacity through a multilayer perceptron neural network using voltage curves measured during constant current (CC) charging, charging times, and initial SOC. Estimation accuracy was verified using two aging experiments. Estimation result shows that the Mean Absolute Error (MAE) is 0.39% and Maximum Error (ME) is 2.12%. In addition, even if there is an error in the estimated initial SOC, the capacity estimation errors are 0.85% MAE and 3.23% ME, showing robust characteristics regarding initial SOC estimate errors. The proposed algorithm is available for high charging currents and various initial SOC conditions, it and estimates capacity with high accuracy, even if there are errors in the estimated initial SOC. Owing to these advantages, the proposed algorithm can be easily implemented in actual applications.

Capacity Estimation of Li-Ion Batteries Using Constant Current Charging Voltage With Multilayer Perceptron