A Holistic Electrothermal Profiles Online Sensing Method With Sparse Sensor System in Large-Format Battery Pack

With the promising advancements in lithium-ion battery technology, the management of large-scale battery packs is confronted with a significant challenge: while comprehensive, time-varying electrothermal profiles are essential, the sparse sensor system offers only limited sensing information. In emerging battery management systems, the lack of information in sensor systems is generally overlooked. This article proposes an optimization method for the architecture of battery sensor systems, enabling online sensing of global electrothermal profiles in large-scale battery packs. First, by expanding the application of information entropy, an architecture optimization method for an arbitrary battery sensor system is proposed. Building on this optimized sensor topology, the article then focuses on an online sensing method to capture global electrothermal profiles in large-scale battery packs. Through this approach, 3-D thermal profiles are derived using an enhanced fractal process, while global electrical parameters are updated in real time, accounting for thermal effects. Finally, this study shows that this article's criteria for optimizing sensor topology can reveal more comprehensive characteristics of the global electrothermal field. The effectiveness of our methodology is validated by online sensing results of the electric and thermal profiles, achieving the error of less than 2% at the pack levels.