理学院 活动议程
时间：9月26日下午2点-3点50分
地点：SD220
活动简介
This talk focuses on the role of AI and multidisciplinary optimization in enhancing battery thermal performance. It addresses two core research problems: parametric and structural optimization. Parametrically, AI algorithms optimize variables like cell spacing, coolant flow rate, and temperature to maximize temperature uniformity and minimize differences within a battery pack. Structurally, deep learning-driven topology optimization discovers the ideal cooling plate or tube geometry of battery. This approach simultaneously maximizes thermal performance while minimizing weight and pressure drop. The proposed method includes integrating advanced AI models, specifically Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP), with topology optimization. This powerful combination generates highly efficient, novel, and optimal cooling channel designs that traditional methods cannot achieve, leading to significant advancements in battery thermal management.
主讲人
Dr. Akhil Garg is an Associate Professor at School of Chemistry and Materials Science at XJTLU. He had 6 years of work experience at HUST, and holds a PhD from Nanyang Technological University, Singapore, which included collaborative research with Rolls-Royce. His expertise lies in applying AI and machine learning to battery technology, specifically in battery synthesis, improving thermal efficiency, and for battery diagnosis.
He has presided over numerous international grants, including ones with Queen Mary University of London and Saint Petersburg State University of Russia. He has been Recognized as a Stanford Elsevier Top 2% Scientist for two consecutive years. He has published over 100 articles and a Scopus H-index of 47.
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