Synthetic Data-Integrated Li-Ion Battery Modeling for eVTOL Energy Systems

Abstract

Verbatim from Semantic Scholar. Not paraphrased, not summarized.

Lithium-ion batteries play a crucial role in present-day energy storage systems, necessitating the development of precise prediction models to improve their performance and ensure safety. The present study introduces a comprehensive methodology that encompasses the calibration, validation, and application of two separate Li-ion battery electrochemical models: the equivalent circuit model and the electrochemistry-based model. The calibration and validation of these models are based on experimental data conducted under various operating conditions, including charge/discharge rates, calendaring temperature, and Hybrid Pulse Power Characterization (HPPC) tests. After the successful validation process, these models are used to generate synthetic data tailored to real-world applications, particularly electric vertical takeoff and landing vehicles (eVTOL). The primary objective is to assess the precision of battery performance prediction, wherein the synthetic data is thoroughly compared with real experimental data. The results demonstrate the effectiveness of the proposed approach in developing a model that reduces dependence on labor-intensive testing and associated equipment costs, reduces time for experimentation, and accelerates controller development for batteries. This work highlights the importance of precise predictive models in lithium-ion batteries, facilitating the effective investigation of practical applications and advancements in energy storage technology.

Authors

• Mohammad Qasem
• Stoyan Stoyanov
• Sadam Ratrout
• Mariana Haddadin
• Yazan Yassin
• Chengxiu Chen
• Said Al‐Hallaj
• Mahesh Krishnamurthy

Keywords

• Computer Science
• Engineering
• Environmental Science