To ensure reliable and safe operation of electric vehicles it is important to estimate the State of Charge (SoC) and State of Health (SoH) of the battery accurately and should be able to implement practically in embedded Battery Management System (BMS). The BMS algorithms must calculate the parameters of the electro-chemical battery system which are difficult to be measured directly. The amount of energy is estimated to calculate the distance the vehicle can travel and the amount of energy required for the next recharge. In this chapter, the method for estimating the SoC and SoH are analyzed in dynamic load conditions. The Sigma point Kalman filter is used for estimating the SoC of a battery which is operated at non-linear conditions and varying temperatures. The Approximate Weighted Total Least Square Algorithm which is computationally less intensive and also has fading memory which gives more emphasis on recent measurements is used for estimating the SoH of battery to reduce the errors in computation. Equivalent circuit-based battery model is implemented using MATLAB/Simulink to estimate the SoC and SoH that are estimated using above-mentioned methods, and the results are validated.