Shengyu Tao

h-index19
2papers
1,595citations

2 Papers

9.6AIFeb 24, 2025
PulseBat: A field-accessible dataset for second-life battery diagnostics from realistic histories using multidimensional rapid pulse test

Shengyu Tao, Guangyuan Ma, Huixiong Yang et al.

As electric vehicles (EVs) approach the end of their operational life, their batteries retain significant economic value and present promising opportunities for second-life use and material recycling. This is particularly compelling for Global South and other underdeveloped regions, where reliable energy storage is vital to addressing critical challenges posed by weak and even nonexistent power grid and energy infrastructures. However, despite this potential, widespread adoption has been hindered by critical uncertainties surrounding the technical performance, safety, and recertification of second-life batteries. In cases where they have been redeployed, mismatches between estimated and actual performance often render batteries technically unsuitable or hazardous, turning them into liabilities for communities they were intended to benefit. This considerable misalignment exacerbates energy access disparities and undermines the broader vision of energy justice, highlighting an urgent need for robust and scalable solutions to unlock the potential. In the PulseBat Dataset, the authors tested 464 retired lithium-ion batteries, covering 3 cathode material types, 6 historical usages, 3 physical formats, and 6 capacity designs. The pulse test experiments were performed repeatedly for each second-life battery with 10 pulse width, 10 pulse magnitude, multiple state-of-charge, and state-of-health conditions, e.g., from 0.37 to 1.03. The PulseBat Dataset recorded these test conditions and the voltage response as well as the temperature signals that were subject to the injected pulse current, which could be used as a valuable data resource for critical diagnostics tasks such as state-of-charge estimation, state-of-health estimation, cathode material type identification, open-circuit voltage reconstruction, thermal management, and beyond.

10.3IRDec 30, 2024
Hgformer: Hyperbolic Graph Transformer for Recommendation

Xin Yang, Xingrun Li, Heng Chang et al.

The cold start problem is a challenging problem faced by most modern recommender systems. By leveraging knowledge from other domains, cross-domain recommendation can be an effective method to alleviate the cold start problem. However, the modelling distortion for long-tail data, which is widely present in recommender systems, is often overlooked in cross-domain recommendation. In this research, we propose a hyperbolic manifold based cross-domain collaborative filtering model using BiTGCF as the base model. We introduce the hyperbolic manifold and construct new propagation layer and transfer layer to address these challenges. The significant performance improvements across various datasets compared to the baseline models demonstrate the effectiveness of our proposed model.