Monitoring system for Li-Ion batteries state of health estimation for smart grid

Abstract

The recent technological developments have evolved the remote and integrated power systems related technologies and tools. The render novel developments of smart and micro grids. The battery is a key element of the modern power systems and is frequently employed in various important applications like hybrid cars, drones, avionics, satellites, and mobile phones. The Li-Ion batteries are extensively employed because of their ever- wanted features like compact size, high power supply capability, a higher number of charge-discharge cycles, etc. Batteries are quite expensive, therefore for an effective utilization of batteries and in order to assure their longer life the Battery Management Systems (BMSs) are frequently employed. The modern BMSs require extensive processing resources which can render into higher power consumption overhead. In this context, several embedded and integrated systems-based solutions have been proposed. This thesis focuses on enhancing the existing Li-Ion BMSs by redesigning their associative data acquisition and processing chain. The focus is to ameliorate the data acquisition and the Li-Ion batteries State of Health (SoH) estimation mechanisms. In this framework, event-driven sensing and processing approaches are used. In contrast to the traditional counterparts, the battery cell parameters are no more captured periodically but are acquired based on events. It results in significant real-time data compression. Afterward, this non-uniformly partitioned information is employed by original event- driven voltage base and Coulomb counting algorithms for a real-time determination and calibration of the cell State of Health (SoH). The system performance is studied with the help of four case studies. Results are used to compare the devised system performance with the traditional counterparts. Preliminary results demonstrate anot able outperformance in terms of compression gain and computational efficiency, for the studied case, while assuring an analogous SoH estimation precision. 7 This work is well aligned with the 2030 vision of Saudi Arabia and the goals of future smart cities like NEOM. It contributes in realizing modern smart – energy related services such as electric vehicles, hybrid power systems, integration of renewable energy sources in smart grid, mitigation of power quality issues, effective dimensioning of renewable energy systems, efficient cell-balancing, and energy storage automatic management and maintenance.