Data-driven modelling and evaluation of a battery-pack system’s mechanical safety against bottom cone impact

Dongxin Xu, Yongjun Pan, Xiaoxi Zhang, Wei Dai, Binghe Liu and Qi Shuai

Energy, 2024, vol. 290, issue C

Abstract: The exploration of the mechanical responses of the battery pack system (BPS) when subjected to dynamic impact loads is crucial for the safety of power batteries during collisions. To elucidate the mechanical response mechanism of the BPS under bottom impact, in this study, a series of finite element (FE) analysis tests were conducted in simulation environments with various combinations of the top radius of the cone model simulating objects, shell thickness, impact velocity, and impact angle. In the FE simulation, the BPS’s nonlinear model was developed and then validated through modal analysis. Subsequently, five machine learning (ML) models enhanced by optimized algorithms were used to evaluate the mechanical response to the bottom impact of BPS. The training data of the ML model is obtained through the optimal Latin hypercube experimental design based on the FE dynamic simulation process. By comparison between cases, the predictive capabilities of various ML models are examined and the robustness of these models are assessed through adding Gaussian noise to ML models. In conclusion, the results demonstrate that ML methods can effectively evaluate the mechanical response of BPS bottom impact and furnish critical design inputs for manufacturing next-generation batteries with durability and safety limitations.

Keywords: Battery-pack system; Mechanical safety; Mechanical response; Bottom impact; Evaluation model; Machine learning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223035399
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:290:y:2024:i:c:s0360544223035399

DOI: 10.1016/j.energy.2023.130145

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().