Nowadays the electric vehicle(EV) has become the main development direction for all OEMs, to meet the challenges of environment and energy saving. The core factor which leads the development and performance of electric vehicle is the use of Lithium battery, that varies the available range of vehicle and its power performance. Various Lithium-ion battery has been introduced into market leading different performance characteristics with increased energy density. From development point of view, different battery development direction requires a reliable system for control and monitoring the status of the battery. From this point the usual challenges of battery condition accuracy occurs •??Long voltage relaxation time to reach its open circuit voltage (OCV) after a current pulse •??Temperature and SOC dependence with Flat OCV-SOC curve for most of the SOC range •??Precise measurement of initial condition from unknown condition due to internal resistance loss This thesis addresses to compare via the modelling approach using an equivalent circuit battery plant with extended Kalman filter and neural network method from machine learning approach to present a robustness result of battery condition estimation comparison.