Browse Publications Technical Papers 2024-28-0032
2024-10-17

Comparative Analysis of GenAI Models for EV Battery Characterization Data Expansion and Validation 2024-28-0032

Rapid advancement of electric vehicle (EV) technology has propelled the need for reliable and efficient methods for battery data expansion and validation. This has vital importance – to ensure safety aspects and efficient design of EV system. Traditional data collection methods for battery characterization are a large subject for the design of experiments and are often expert’s skill intensive, time-consuming, and lack scalability. This study proposes a Generative Artificial Intelligence (GenAI) based approach for two activities – First to assist the DOE of cell/battery characterization at different C rates and temperatures accounting for varied degradation rates. Secondly, manipulations of characterization data accounting for measurement and data recording errors. The study compares GenAI models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based (Time-GPT) models in generating and validating EV battery characterization data. This is not a complete replacement for battery testing operations as physically battery needs to undergo cyclic aging and other testing operations. The paper explores the ability of different GenAI models to accurately capture critical electro-chemical and thermal features. This enables better planning of next characterization experiments and assist in eliminating boundary and intermediate scenarios for cell characterization experiments by generating synthetic data from GenAI models. The data manipulations re-establish battery characterization data generated from testing experiments accounting for sensor issues, data logging issues, data transportation and synchronization issues, etc. The robustness of these models in handling diverse, heterogeneous, and asynchronous datasets sourced from different EV manufacturers, battery chemistries, and specifications are scrutinized. The performance of the models is compared across multiple attributes like execution times, computing resource requirements, accuracy and consistency of generated data, and volume of data required to optimize the models. This study contributes to improving EV battery modelling, simulation, and optimization, facilitating rapid development of data-based products specific to battery health analytics.

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