Data Driven Analytics of Porous Battery Microstructures

2021 ◽  
Author(s):  
Abhas Deva ◽  
Vojtech Krs ◽  
Lucas Robison ◽  
Carl Adorf ◽  
Bedrich Benes ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Data Driven ◽  
Trial And Error ◽  
Experimental Trial ◽  
Microstructural Optimization

The microstructural optimization of porous lithium ion battery electrodes has traditionally been driven by experimental trial and error efforts, based on anecdotal understanding and intuition, leading to the development of...

A generalized additive model-based data-driven solution for lithium-ion battery capacity prediction and local effects analysis

2021 ◽  
pp. 014233122110579
Author(s):  
Tao Chen ◽  
Ciwei Gao ◽  
Hongxun Hui ◽  
Qiushi Cui ◽  
Huan Long
Keyword(s):  
Energy Storage ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Additive Models ◽  
Data Driven ◽  
Local Effects ◽  
Interaction Terms ◽  
Battery Capacity ◽  
Formulation Parameters ◽  
Capacity Prediction

Lithium-ion battery-based energy storage systems have been widely utilized in many applications such as transportation electrification and smart grids. As a key health status indicator, battery performance would highly rely on its capacity, which is easily influenced by various electrode formulation parameters within a battery. Due to the strongly coupled electrical, chemical, thermal dynamics, predicting battery capacity, and analysing the local effects of interested parameters within battery is significantly important but challenging. This article proposes an effective data-driven method to achieve effective battery capacity prediction, as well as local effects analysis. The solution is derived by using generalized additive models (GAM) with different interaction terms. Comparison study illustrate that the proposed GAM-based solution is capable of not only performing satisfactory battery capacity predictions but also quantifying the local effects of five important battery electrode formulation parameters as well as their interaction terms. Due to data-driven nature and explainability, the proposed method could benefit battery capacity prediction in an efficient manner and facilitate battery control for many other energy storage system applications.

Data-driven analysis on thermal effects and temperature changes of lithium-ion battery

2021 ◽  
Vol 482 ◽  
pp. 228983
Author(s):  
Shan Zhu ◽  
Chunnian He ◽  
Naiqin Zhao ◽  
Junwei Sha
Keyword(s):  
Lithium Ion Battery ◽  
Thermal Effects ◽  
Lithium Ion ◽  
Data Driven ◽  
Temperature Changes

Toward Data‐Driven Applications in Lithium‐Ion Battery Cell Manufacturing

2019 ◽  
Vol 8 (2) ◽  
pp. 1900136 ◽  
Author(s):  
Artem Turetskyy ◽  
Sebastian Thiede ◽  
Matthias Thomitzek ◽  
Nicolas von Drachenfels ◽  
Till Pape ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Data Driven ◽  
Battery Cell ◽  
Cell Manufacturing
Sign in / Sign up

Export Citation Format

Share Document