Electrochemical batteries open possibilities for cost optimization in microgrids and electricity distribution grids as long as the planned energy exchange profile between the battery system and the remaining grid is strictly adhered to. The valve-regulated lead-acid (VRLA) batteries have almost suppressed out of use the flooded lead-acid batteries and got an important role in microgrids and power supply systems in telecommunications. Choosing the proper charging and discharging methods for this type of batteries is a very important aspect to economically justify their installation, minimize the degradation effects inside them, and to have their lifetime as declared by the manufacturer. In order to properly control the charging and discharging of the batteries, it is important to accurately determine the values of their states and parameters. Firstly, in the doctoral thesis the joint estimation of battery states and parameters using the Sigma-point Kalman filter (SPKF) is preformed where the pre-identification experiment was carried out for parameter identification. After that, joint estimation of battery states and parameters is performed where the initial states and parameters for SPKF algorithm are calculated from the manufacturer data sheet of the battery. The experimental tests are performed using a 48 V VRLA battery stack. After the joint estimation is preformed, a control algorithm for battery charging using model predictive control is created where the upper threshold voltage level, the maximum battery temperature and/or the maximum battery temperature increase compared to the ambient temperature, the maximum charge current and the maximum state of charge are used as constraints. The main objective of the proposed algorithm is to fully charge the VRLA battery stack as fast as possible without violating constraints on its variables provided by the manufacturer. In the doctoral thesis the application of the energy storage systems i.e. the batteries in microgrid is also analyzed. The battery management system is one of the subordinated systems to the microgrid energy flow optimization. The energy flow optimization yields an energy flow command towards the subordinated battery management system. Firstly, the approaches of the optimal battery current, constant current and constant power to the battery management system operation are compared by simulations. Then the battery management procedure based on optimal control paradigm for efficient adherence to the issued energy exchange commands is developed. The proposed battery management system retains the states and inputs of the batteries within the longevity constraints provided by the manufacturer in order to reduce their degradation effects and prolong their lifetime. After presenting the method for solving the posed optimization problem, its application within a closed-loop feedback control system for charging and discharging of the VRLA battery stack is carried out. The effectiveness of the approach and the feasibility of real-time implementation are demonstrated both in simulations and experimentally. Throughout the thesis the hybrid electrical model, which is the most precise of all battery electrical models, is used for modelling the behavior of the considered VRLA battery stacks.