Abstract
Today, the storage of electrical energy is one of the most important technical challenges. The increasing number of high capacity, high-power applications, especially electric vehicles and grid energy storage, points to the fact that we will be faced with a large amount of batteries that will need to be recycled and separated in the near future. An alternative method to the currently used methods for separating these batteries according to their chemistry is discussed in this study. This method can be applied even on integrated circuits due to its ease of implementation and low operational cost. In this respect, it is also possible to use it in multi-chemistry battery management systems to detect the chemistry of the connected battery. For the implementation of the method, the batteries are connected to two different loads alternately. In this way, current and voltage values are measured for two different loads without allowing the battery to relax. The obtained data is pre-processed with a separation function developed based on statistical significance. In machine learning algorithms, artificial neural network and decision tree algorithms are trained with processed data and used to determine battery chemistry with 100% accuracy. The efficiency and ease of implementation of the decision tree algorithm in such a categorization method are presented comparatively.
Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation*
