Lithium‐ion batteries have a very wide application range. They can power up small electronic devices such as smart watches to larger electric vehicles. Due to its varied range of applications, they come in different packaging and in such battery packs, even when individual cell voltage exceeds by a few milli‐volts above 4.2 V, it may result in thermal runaway and explode the cell. During discharge cycle, cell imbalances hinder the use of battery to its full capacity. This in turn decreases the battery lifetime. The individual battery cells should be equalized on a regular basis to keep the imbalances to a minimum and to have a good battery life. The process of balancing the individual cell charges by measuring the cell state of charge (SoC) and its voltage in a battery pack is known as cell balancing. This paper details an active cell balancing technique that uses a buck converter for balancing a series connected battery pack of lithium‐ion cells. A buck converter along with a pair of MOSFET switches for each cell, one turned on for charging the cell and the other one turned on while discharging the cell is used in this experiment. An algorithmic model suitable for reconfigurable battery systems that measures the individual cell voltages and is developed for balancing a pack of series connected Li‐ion battery cells. The developed model is simulated using MATLAB for verifying its performance. A state of charge of 25% is maintained across the cells and when SoC value drops below this even a difference of 0.02% is sensed by the algorithm to initiate balancing function. This balancing is found to take 275 ms to balance three 3.7 V batteries and thus the model is found to respond faster. The results show that this method can self‐adaptively attain satisfactory performance within a limited equalizing period.

Power system needs a continuous upgrade to overcome the challenges like distributed control, self-healing, power quality, demand side management and integration of renewable system. At present, power system needs an advance and intelligent technology to perform various system level tasks. Centralized control of the system has efficient operation during integration of the renewable resources and lag of communication between the stations. Smart grid provides the intelligent and efficient power management system. Upgrade of present power system with multi-agent system (MAS) provides the solution for most of the power system issues. More number of MAS are used in the power system network based on acquires of the system. MAS are communicating with each other for the more acquired result. Better implantation of MAS can achieved by providing the high speed and secured communication protocol. In this chapter, we discussed about the MAS fundamental architecture and intelligent controller design tools and case study of real time tariff management using MAS.

As Electric Vehicles (EVs) are environmentally friendly, there is an urgent need for widely dispersed charging stations, particularly because of their small on-board battery capacity. However, the utility grid is subject to a high energy demand due to the charging stations that are necessary to charge the batteries of EVs. Energy in the form of Photovoltaic (PV) energy, is one of the main strategies to diminish the strain on the grid. This study focusses into evaluate the contribution of PV in EV charging (PV powered) infrastructures through improved energy management. Due to the power generated from solar system is random in nature, there is a need for rigorous control design and optimization to maintain system stability. Therefore, a PV based Luo converter with Particle Swarm Optimized Proportional Integral (PSO-PI) controller was developed. The voltage of PV system is enhanced by utilizing Luo converter with better voltage gain. Subsequently, the voltage is stabilized by a PI controller and its performance is enhanced by a PSO algorithm. Consequently, the Three Phase (3ϕ) Voltage Source Inverter (VSI) supplies the power to the Brushless Direct Current (BLDC) motor. To verify the performance of the developed research, MATLAB/Simulink tool. Were used. The developed converter attains the efficacy of 95.76%, thereby enhancing the sustainability of overall system.