||Study of Battery State-of-charge Estimation with kNN Machine Learning Method
||(Teressa Talluri) ; (Hee Tae Chung) ; (Kyoojae Shin)
|| Electrical vehicle; Battery characteristics; State of charge; Depth of discharge; Machine learning algorithms and kNN method
||Electric vehicles have high demand due to their ecofriendly nature. From this point of view, lithium batteries have gained high attention in recent days due to their high efficiency and long life time. Hence, it is of the utmost importance to evaluate the battery characteristics, such as the state of charge (SOC), depth of discharge (DOD), and remaining life of a battery to ensure battery safety. These parameters were derived in order to estimate the battery life time before degradation. This estimation is very much required in making a decision about battery usage in the future. In this study, the SOC of a lithium polymer battery was evaluated in a real-time experiment. Charging and discharging cycles were done, and we obtained the voltage, current, and time data from the experimental result. This experimental data trained machine learning methods such as the kNN (k Nearest Neighbor) method to estimate the SOC more precisely. After training the model, a test was done. The proposed estimator was calibrated by experimental data. The results are satisfactory with accuracy of 98% and mean absolute error (MAE) as low as 0.74[%].