A novel state-of-charge estimation method of lithium-ion batteries combining the Grey model and Genetic Algorithms

In order to guarantee safe and reliable operation of battery in electric vehicles and utilizing capacity at the greatest extent, it is indispensable to estimate the state-of-charge (SoC) of battery. This study aimed to develop such a novel estimation approach based on the Grey model and Genetic Algorithms method without the need of a high computation cost and high-fidelity battery model. A SoC analytical model was established using the Grey System theory based on a limited amount of incomplete data compared to conventional methods. The model was further improved by applying the sliding window mechanism to adjust the model parameters according to the evolving operating status and conditions. In addition, the Genetic Algorithms were introduced to achieve an optimal adjustment coefficient, < formula > < tex > $\lambda$ < /tex > < /formula > , in the traditional Grey model (1, 1) model to further improve the source estimation accuracy. For experimental verification, two types of Lithium-ion batteries were used as the device-under-test, and the accuracy and repeatability of the proposed modeling method were verified under a range of battery discharging conditions. The results indicate that the proposed modeling approach features a higher accuracy for such systems compared to the benchmarking GM method that is illustrated using typical passenger car driving cycles.