Harnessing digital twin and IoT for real-time monitoring, diagnostics, and error correction in domestic solar energy storage

This study introduces a cloud-based platform designed for real-time monitoring and comprehensive analysis of lithium-ion battery performance, incorporating a digital twin Battery Management System (BMS). This system overcomes the limitations of traditional local BMS, especially in historical data analysis. It employs advanced data processing and analytics to improve battery performance and enhance prediction accuracy. Key components include a energy measurement correction method, an coulombic efficiency (CE) estimation technique, and SOC estimation using an optimizer algorithm. These strategies are crafted to address sensor errors and dynamically adjust estimations to minimize inaccuracies. The use of sophisticated algorithms to optimize the objective function has led to significant experimental outcomes, notably in reducing the Mean Square Error (MSE) in estimations. The paper also introduces various novel methods for estimating irregular battery data, using the Central Limit Theorem for improved precision. Experimentally, the system identified a battery CE of 0.978 for a specific battery, demonstrating its capability in monitoring battery health. These advancements offer substantial scholarly insights and pave the way for broader application of advanced digital twin BMS technologies in residential battery storage and other areas. The synergy of this system with other smart grid technologies envisions a future where energy storage and management are not only more efficient and reliable but also finely optimized, enhancing the tracking and management of battery life cycles.