In order to guarantee safe and reliable operation of electric vehicle batteries and to optimize their energy and capacity utilization, it is indispensable to estimate their state-of-charge (SoC). This study aimed to develop a novel estimation approach based on the grey model (GM) and genetic algorithms without the need of a high-fidelity battery model demanding high computation power. A SoC analytical model was established using the grey system theory based on a limited amount of incomplete data in contrast with conventional methods. The model was further improved by applying a sliding window mechanism to adjust the model parameters according to the evolving operating status and conditions. In addition, the genetic algorithms were introduced to identify the optimal adjustment coefficient λ in a 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 that underwent typical passenger car driving cycles. The proposed SoC estimation method were verified under diverse battery discharging conditions and it demonstrated superior accuracy and repeatability compared to the benchmarking GM method.