State of charge (SOC) is one of the crucial assessment indexes in the battery storage management system and has gained attention for lithium-ion battery due to its lucrative characteristics of fast charging, high voltage, high energy density, and long-life cycle. However, accurate SOC estimation is a serious concern due to the lithium-ion battery nonlinear characteristics and complex electrochemical reactions. The existing SOC estimation techniques suffer from high computational efforts and cannot deliver accurate results due to various uncertainties such as temperature, noise and aging. Therefore, the objective of this book is to develop an enhanced SOC estimation method using an artificial intelligent algorithm. Recurrent nonlinear autoregressive with exogenous inputs (RNARX) algorithm is a well-known subclass of artificial intelligent algorithm that has received popularity in designing complex and nonlinear system due to its improved learning capability, convergence speed, generalization performance and high accuracy in this book, RNARX algorithm-based lightning search algorithm (LSA) is developed to enhance SOC estimation accuracy. Furthermore, the performance of RNARX based LSA algorithm for SOC estimation is compared with state of art artificial intelligent algorithms including back-propagation neural network (BPNN), radial basis function neural network (RBFNN), extreme leaming machine (ELM), and deep recurrent neural network (DRNN) and random forests (RFs) algorithm. Therefore, RNARX-LSA based SOC estimation model has great potential to be implemented in real-time electric vehicle battery storage systems