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contributor authorLi, Bing
contributor authorJones, Casey M.
contributor authorTomar, Vikas
date accessioned2022-02-06T05:38:02Z
date available2022-02-06T05:38:02Z
date copyright6/14/2021 12:00:00 AM
date issued2021
identifier issn2381-6872
identifier otherjeecs_18_4_040905.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4278439
description abstractThis work focuses on the use of linear regression analysis-based machine learning for the prediction of the end of discharge of a prismatic Li-ion cell. The cell temperature was recorded during the cycling of Li-ion cells and the relation between the open circuit voltage (OCV) and cell temperature was used in the development of the linear regression-based machine learning algorithm. The peak temperature was selected as the indicator of battery end of discharge. A battery management system (BMS) using a pyboard microcontroller was constructed to monitor the temperature of the cell under test and was also used to control a MOSFET that acted as a switch to disconnect the cell from the circuit. The method used an initial 10 charge and discharge cycles at a rate of 1C as the training data, then another charge and discharge cycle for the testing data. During the test cycling, the discharge was continued beyond the cutoff voltage to initiate an overdischarge while the temperature of the cell was continuously monitored. When the temperature of the cell exceeded the predetermined threshold, the pyboard triggered the MOSFET to disconnect the cell and stop the overdischarge. The experiment was performed on three different cells, and the overdischarge for each was secured within 0.1 V of the cutoff voltage. The results of these experiments show that a linear regression-based analysis can be implemented to detect an overdischarge condition of a cell based on the anticipated peak temperature during discharge.
publisherThe American Society of Mechanical Engineers (ASME)
titleOverdischarge Detection and Prevention With Temperature Monitoring of Li-Ion Batteries and Linear Regression-Based Machine Learning
typeJournal Paper
journal volume18
journal issue4
journal titleJournal of Electrochemical Energy Conversion and Storage
identifier doi10.1115/1.4051296
journal fristpage040905-1
journal lastpage040905-5
page5
treeJournal of Electrochemical Energy Conversion and Storage:;2021:;volume( 018 ):;issue: 004
contenttypeFulltext


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