Advancing Thermal Management Through Generalized Neural Network Regression
Conventional Computational Fluid Dynamics (CFD) simulations are the gold standard for thermal analysis in Lithium-Ion Battery Systems, but their extensive computational requirements lead to inefficiencies in iterative design processes.
The adoption of Generalized Neural Network Regression (GNNR) introduces a paradigm shift, enabling rapid thermal modeling without compromising on accuracy. This approach leverages machine learning architectures to predict complex heat dissipation patterns, significantly reducing simulation overhead. By integrating GNNR, Battery Management Systems (BMS) can now benefit from streamlined workflows, enhanced predictive accuracy, and scalable deployment across varying battery architectures