Modeling and optimization of electro-mechanical properties in 18,650 batteries using a hybrid DNN-GWO approach: integrating metrological data and simulation

Abstract

The process of optimizing lithium-ion batteries needs precise assessment of all their electro-mechanical and vibrational characteristics. The researchers present a measurement-based framework which enhances the performance of 18,650 lithium-ion cells that use graphene nanoplatelets as reinforcement material. A deep neural network (DNN) serves as the system that establishes complex connections between structural elements and their resulting performance metrics which include mechanical strength and electrical conductivity and vibrational response. The Grey Wolf Optimizer (GWO) establishes a structured approach to measurement space exploration which identifies the best system setups that achieve maximum battery capacity while maintaining dependable measurement results. The method focuses on three key aspects which include accurate measurement results and consistent measurement outcomes and methods of measuring uncertain results. The strong metrological framework establishes assessment procedures for evaluating performance improvements. The framework's ability to predict future outcomes gets confirmed through testing with separate experimental data from academic research while high-fidelity simulations show complete details about the electro-mechanical performance. The approach uses metrological principles which include accuracy and repeatability and uncertainty quantification to create a modeling and optimization system which trains the DNN using a hybrid dataset that combines experimental data with high-fidelity simulations and includes dedicated measurement noise simulation through explicit data augmentation. The measurement-guided approach achieves better results because it combines two advantages: improved accuracy and enhanced computational efficiency which surpass standard predictive and optimization methods. The research introduces an innovative measurement-based technique which uses artificial intelligence together with metrological standards to deliver precise battery performance evaluations and structured development of lithium-ion batteries.