Artificial Intelligence-Based Smart Battery Management System for Solar Grid Integrated Microgrids

As renewable energy, microgrids, and electric vehicles (EVs) continue to advance at a rapid pace, batteries have taken centre stage as the primary energy storage solution. However, batteries are expensive and require special consideration especially lithium-ion batteries that can burn because of over charging/discharging. Battery management systems (BMS) play a critical role in the widespread adoption of these technologies by managing the operations of the storage device to optimise its longevity, effectiveness, and safety. Therefore, this study proposes a smart BMS for grid-connected microgrids based on AI techniques that can control the battery charge\discharge cycle efficiently providing optimal real-time decisions for safer operations and to maximise the batteries lifetime. The proposed system uses a grey wolf optimiser to control the battery charge and discharge based on short-term forecasting data. Experimental results exhibit the robustness of the proposed strategy in handling uncertainties and achieving optimal battery utilisation. An experimental microgrid configuration is created to imitate real-world outdoor conditions and assess the proposed system performance. A promising performance is shown where the system supplied the load with 100% of energy when it is sunny. The study’s findings are important for advancing traditional microgrid industry smart BMS.