Li‐ion batteries are a critical energy source for both fixed and mobile systems and represent an important component in future carbon‐footprint mitigation strategies. However, energy system architecture and operating environments significantly influence battery lifetime, making effective battery management a challenging task. Despite recent advances in diagnostic tools and modelling software, there remains a growing opportunity to integrate battery‐related data and transmit it to cloud platforms through Internet of Things (IoT) applications. To develop a digital twin (DT) representation of battery packs, this paper proposes a cloud‐based battery management system, where diagnostic algorithms analyze operational data to estimate battery charge status, lifecycle behavior, and overall condition. An enhanced coulomb counting–based state‐of‐charge estimation method is introduced and demonstrated to operate effectively for Li‐ion batteries, achieving a satisfactory reduction in estimation error. In parallel, large‐scale data available on cloud platforms may be leveraged for system prediction and optimization using machine‐learning techniques, offering new perspectives on battery lifetime assessment. By integrating these emerging approaches, a smart and interconnected battery management framework can be realized. The proposed system identifies key functions required for routine battery operation and management, enabling batteries to operate reliably beyond their nominal economic lifetime.
Manohar et al. (Thu,) studied this question.
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