AI-Driven Digital Twin Model for Reliable Lithium-Ion Battery Discharge Capacity Predictions

Abstract

The present study proposes a novel method for predicting the discharge capabilities of lithium-ion (Li-ion) batteries using a digital twin model in practice. By combining cutting-edge machine learning techniques, such as AdaBoost and long short-term memory (LSTM) network, with a semiempirical mathematical structure, the digital twin (DT)—a virtual representation that mimics the behavior of actual batteries in real time is constructed. Various metaheuristic optimization methods, such as antlion, grey wolf optimization (GWO), and improved grey wolf optimization (IGWO), are used to adjust hyperparameters in order to optimize the models. As indicators of performance, mean absolute error (MAE) and root-mean-square error (RMSE) are applied to the models after they have undergone extensive training and ten-fold cross-validation. The models are rigorously trained and cross-validated using the NASA battery aging dataset, a widely accepted benchmark dataset for battery research. The IGWO-AdaBoost digital twin model emerges as the standout performer, achieving exceptional accuracy in predicting the discharge capacity. This model demonstrates the lowest mean absolute error (MAE) of 0.01, showcasing its superior precision in estimating discharge capabilities. Additionally, the root mean square error (RMSE) for the IGWO-AdaBoost DT model is also the lowest at 0.01. The findings of this study offer insightful information about the potential utilization of the digital twin model to accurately predict the discharge capacity of batteries.

A Data-Driven Model for Power Loss Estimation of Magnetic Materials Based on Multi-Objective Optimization and Transfer Learning

“…Utilizing Optuna for MOO, we focus on two primary objectives: reducing the validation loss to enhance model accuracy and minimizing the number of parameters. Different hyperparameters tuning methods have been analyzed in [30,31], and NSGA-II (Non-dominated Sorting Genetic Algorithm II) is favored in this study as the optimization engine for its effective balance in ranking and diversity preservation in MOO contexts [32]. The search space is defined as follows: the number of middle layers in the FNN structure varies between 2 and 5, each containing 8 to 64 neurons.…”