<p>Model uncertainties and measurement sensor mistakes have a substantial influence on the estimation process and reduce the accuracy of identifying the state of charge (SoC) of lithium-ion batteries (LIBs). To solve this issue, this article uses an adaptive augmented sliding mode method, in which measurement faults are considered as an additive state variable. Besides, the problem of resistance to model uncertainties is also solved by designing this estimator from the sliding family. The innovation of this article is the ability to estimate measurement errors at the same time as estimating the battery charge level. Conversely, the estimator presented in this article, for the first time, has been adapted in such a way as to eliminate the chattering phenomenon to a great extent and raise the precision of the estimation. In other words, to adapt the gain of the estimator based on the estimation error, a dynamic is extracted. Using this new dynamic, the gain of the estimator is adjusted dynamically at every moment. On the other hand, for the first time, the measurement error of voltage or current sensors is considered as an additional term in the state space model, and the adaptive estimator estimates this additional term. Therefore, the method presented in this article is a combination of adaptive and augmented tactics based on sliding estimators, which is presented for the first time. To assess the productivity of the suggested tactic, its simulation has been validated with a set of practical data in two different phases. The results indicated that the suggested method outperforms the conventional sliding method in terms of accurately calculating the terminal voltage and charge level, with improvements of percentage and two volts, respectively.</p>