Lithium Ion Battery
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Tao Chen ◽  
Ciwei Gao ◽  
Hongxun Hui ◽  
Qiushi Cui ◽  
Huan Long

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.

M. Venkata Ratnam ◽  
K. Senthil Kumar ◽  
S. Samraj ◽  
Mohammedsani Abdulkadir ◽  
K. Nagamalleswara Rao

2021 ◽  
Vol 42 (12) ◽  
pp. 1703-1716
Yifei Qian ◽  
Bo Lu ◽  
Yinhua Bao ◽  
Yanfei Zhao ◽  
Yicheng Song ◽  

2021 ◽  
pp. 2102233
Ye Shui Zhang ◽  
Nicola E. Courtier ◽  
Zhenyu Zhang ◽  
Kailong Liu ◽  
Josh J. Bailey ◽  

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7954
Robby Dwianto Widyantara ◽  
Muhammad Adnan Naufal ◽  
Poetro Lebdo Sambegoro ◽  
Ignatius Pulung Nurprasetio ◽  
Farid Triawan ◽  

Temperature management for battery packs installed in electric vehicles is crucial to ensure that the battery works properly. For lithium-ion battery cells, the optimal operating temperature is in the range of 25 to 40 °C with a maximum temperature difference among battery cells of 5 °C. This work aimed to optimize lithium-ion battery packing design for electric vehicles to meet the optimal operating temperature using an air-cooling system by modifying the number of cooling fans and the inlet air temperature. A numerical model of 74 V and 2.31 kWh battery packing was simulated using the lattice Boltzmann method. The results showed that the temperature difference between the battery cells decreased with the increasing number of cooling fans; likewise, the mean temperature inside the battery pack decreased with the decreasing inlet air temperature. The optimization showed that the configuration of three cooling fans with 25 °C inlet air temperature gave the best performance with low power required. Even though the maximum temperature difference was still 15 °C, the configuration kept all battery cells inside the optimum temperature range. This finding is helpful to develop a standardized battery packing module and for engineers in designing low-cost battery packing for electric vehicles.

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