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Battery type determination can be crucial in some cases such as multi-chemistry battery management systems, second life applications or recycling. The determination process is a challenging problem because voltage readings of batteries with different chemistries would be very close and overlap at certain points depending on their state of charge and state of health. In order to overcome this issue, a method proposed in this study. This method consist of three steps. Step one is data acquisition by measuring terminal voltages and corresponding instant currents under switching loads without relaxation. Step two is merging the data into frames and preprocessing it with the developed separation function based on statistical significance. Step three is training the artificial neural network which is designed to run on a microcontroller. Three types of batteries with different chemical compositions were used for this purpose. These types are the ones that first generation of electric vehicles on the market were commonly equipped. Experimental data acquired for all batteries under varying pulsed load, and statistical significance test, tTest has performed on the data in binary combinations. Here voltage data of LiFePO4 and NCR batteries has found statistically significant. Correspondingly, a separation function has developed for the separation of overlapping data. The preprocessed data with the proposed separation function has used to train an artificial neural network. Results show that, preprocessing the data results to 100% accuracy of battery type determination even on a tiny neural network. © 2021 IEEE. |
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