.png){kind=icon}
返回
Incremental Battery Model Using Wavelet-Based Neural Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/TCPMT.2011.2144983.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a multi-resolution modeling approach using wavelet neural networks. A lithium battery model, which has three resolutions, is developed to depict the modeling approach. By combining the advantages of dyadic activation functions and the orthonormal property of wavelet functions, the developed battery model possesses two salient features. First, the model is built from a coarser approximation to a finer representation by adding more details incrementally. Second, the model at a low resolution is compatible with the model at a high resolution, which means that the parameters used in a low resolution can be directly incorporated into a high resolution without any modification. This paper's results show that this battery modeling provides great flexibility for users to choose a suitable resolution to meet their requirements for model accuracy and model execution speed.
Keyword:
Battery modeling
incremental modeling
multi-resolution
neural networks
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:
3
论文数:
230
被引数:
8.0K
机构
引用论文
High-Yield Preparation of ZnO Nanoparticles on Exfoliated Graphite as Anode Material for Lithium Ion Batteries and the Effect of Particle Size as well as of Conductivity on the Electrochemical Performance of Such Composites.png)
Batteries
OAAI
BatteriesIF0
OAAI分享收藏Preliminary Evaluation of an Injectable Sensor for Subcutaneous Photoplethysmography in Animals
PREAI
分享收藏 没有更多内容
没有更多内容