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A Comprehensive Overview of the Impacting Factors on a Lithium-Ion-Battery’s Overall Efficiency

Kremzow-Tennie Simeon, Scholz Tobias, Pautzke Friedbert, Popp Alexander, Fechtner Heiko, Schmuelling Benedikt

Power Electronics and Drives

2022

Vol. 7 (42)

9--28

This comprehensive overview of the impacting factors on lithium-ion-battery’s (LIB) overall efficiency presents the most relevant influencing factors on a battery’s performance. Dissected into their respective short-term and long-term influences, the working principles behind the efficiency influencing factors are presented. With a strong focus on battery characterisation, charge-profiles and battery management systems (BMSs), the authors present results of their own practical research with a detailed literary analysis, allowing a broad coverage of the complex topic. Finally, the authors present a principle model that indicates the interactions be-tween the different involved components of the battery.

Linear discriminant analysis based on gas chromatographic measurements for geographical prediction of USA medical domestic cannabis

Al Bakain Ramia Z., Al-Degs Yahya S., Cizdziel James V., Elsohly Mahmoud A.

Acta Chromatographica

2021

Vol. 33, no. 2

179--187

Fifty four domestically produced cannabis samples obtained from different USA states were quantitatively assayed by GC–FID to detect 22 active components: 15 terpenoids and 7 cannabinoids. The profiles of the selected compounds were used as inputs for samples grouping to their geographical origins and for building a geographical prediction model using Linear Discriminant Analysis. The proposed sample extraction and chromatographic separation was satisfactory to select 22 active ingredients with a wide analytical range between 5.0 and 1,000 µg/mL. Analysis of GC-profiles by Principle Component Analysis retained three significant variables for grouping job (Δ9-THC, CBN, and CBC) and the modest discrimination of samples based on their geographical origin was reported. PCA was able to separate many samples of Oregon and Vermont while a mixed classification was observed for the rest of samples. By using LDA as a supervised classification method, excellent separation of cannabis samples was attained leading to a classification of new samples not being included in the model. Using two principal components and LDA with GC–FID profiles correctly predict the geographical of 100% Washington cannabis, 86% of both Oregon and Vermont samples, and finally, 71% of Ohio samples.