.png){kind=icon}
返回
Process insights with physics-inspired data-driven modeling- example of battery electrode processing
.png){kind=icon}
OA
AI
DOI：10.1016/j.est.2023.109046.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Lithium-ion batteries are essential for a wide range of applications due to their high energy density and rechargeability. However, their production and performance improvement often rely on time-consuming and expensive experiments. Typically, simulation analyzes is used to build process models to optimize battery performance and lifetime. However, simulations cannot always take into account the limitations of the manufacturing process. As a result, the process parameters determined by such a model remain largely theoretical. For the efficient production of lithium-ion batteries, an understanding of the relationships between physical quantities is essential to meet optimized performance and safety standards. However, these relationships are quite complex, making it difficult for traditional methods to determine the physical model. Alternatively, artificial intelligence methods can be beneficial. This work uses a novel gray-box modeling technique that incorporates physical knowledge and empirical data. To achieve this goal, we combine a deep neural network with a genetic algorithm to determine the existing physical relationships within the data and then estimate the final model for the system.
Keyword:
Physics-inspired data -driven modeling
Genetic algorithm
Dimensional analysis
Deep neural networks
Hybrid modeling
Gray-box technique
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.8
论文数:
2.3W
被引数:
10.1W
机构
引用论文
An approach for optimizing multi-objective problems using hybrid genetic algorithms一种基于混合遗传算法的多目标优化方法.png)
SOFT COMPUTINGOAAI
SOFT COMPUTINGIF2.5
OAAI分享收藏Lithium-Ion Battery Operation, Degradation, and Aging Mechanism in Electric Vehicles: An Overview电动汽车中锂离子电池的运行，退化和老化机理: 概述ENERGIES
OAAI
ENERGIESIF3.2
OAAI分享收藏Genetic Algorithm and Taguchi Method: An Approach for Better Li-Ion Cell Model Parameter IdentificationBATTERIES-BASELOAAI
BATTERIES-BASELIF4.8
OAAI分享收藏Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis用于锂离子电池建模和预测的混合物理信息神经网络OAAI
OAAI分享收藏
查看更多内容