Energy Efficiency and Sustainability of Adaptive Intelligent Battery Management Technologies for EVs and ESSs

Abstract

As an essential component of electric vehicles (EVs), battery surely requires not only the highest level of longevity and zero-death safety but also energy efficiency and sustainability. We propose energy-efficient and sustainable artificial intelligence (AI) battery management technologies that can extend lifetime, reliability, and safety of battery applications and are applicable to all types of rechargeable secondary batteries such as solid- and liquid-type cells. EV battery typically consists of over thousands of cells. The proposed battery architecture provides the ability to adaptively reconfigure battery cell array network in real time as needed. New AI battery technologies evenly drain charge across all battery cells and provide extra battery cells that replace faulty cells in a timely manner, extending the battery lifetime by 7.5%, 9.1%, respectively, and 19.4% when used together while ensuring a required level of voltage and power under dynamic thermal control. The proposed technologies further support three eco-friendly modes with high energy-efficiency and fire-safety. The proposed technologies can also be applied to reliable, remote, yet self-sustainable energy sources such as energy storage systems even with heterogeneous battery cells which are of key importance for decentralization of megalopolises and regionalization.