The purpose of this project is to create and simulate a self-adaptive battery observer using Matlab Simulink. The first phase of the project involves the research of a suitable lithium-ion cell model to cover a wide range of battery based technologies. After selecting the optimum one, the same real lithium-ion cell must undergo experimentation to acquire real current and terminal voltage data to be later on used in the simulation. Afterwards, a model to describe the internal dynamics of the lithium-ion processes must be chosen. The parameters of the model state-space equation must be estimated at every selected state of charge point, preferably more populated at high and low state of charge to improve its accuracy. To estimate the parameters of the selected lithium-ion cell, a pulse discharge test is performed on the cell simulation in Matlab Simulink. The acquired data is processed using the Curve Fitting tool. Look-up-tables are populated using the acquired parameter data as a function of the state of charge. A main model named Self-adaptive battery observer contains 3 different blocks: Coulomb Counting + Extended Kalman Filter correction block, Lithium Cell block and Extended Kalman Filter block. Coulomb Counting + Extended Kalman Filter correction block calculates the actual state of charge using the experimentally measured current while the output state of charge will be calibrated using the estimated state of charge provided by the Extended Kalman Filter. The actual state of charge enters in the Lithium Cell referenced model and is used as the breakpoints at each sample time for the look-up-table mentioned before. Within this block there is another subsystem that uses the parameter estimations provided by the look-up-tables to simulate the transient voltage. This transient voltage is subtracted to the experimentally measured terminal voltage to obtain the calculated open circuit voltage of the system which is compared to the measured from the current pulse discharge test and the error is used in the covariance settings of the Extended Kalman Filter. The Kalman Filter block has two function inputs. The state space function and the measurement function. The first one provides the actual state of charge and the second one the calculated open circuit voltage. Due to the non-linear relationship between OCV and SOC the Extended Kalman Filter has to be used instead of the regular Kalman Filter. Extended Kalman Filter estimates the SOC that is used in the Coulomb Counting block to calibrate the actual SOC. In the end, the code generated should be able to be integrated in the basic software of a Battery Management System (BMS).