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Salami Boluwatito - Final Thesis.pdf (2.72 MB)

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The Efficiency Measuring Apparatus for Li-ion Battery Equalizers

Salami, Boluwatito Peter
http://orcid.org/0000-0002-7875-3091
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1619460723390441

Abstract Details

2021, Master of Science, University of Toledo, Electrical Engineering.
The market for electric vehicles is continually growing. Electric vehicles are mostly powered by Li-ion batteries because of their high power and energy density. The Li-ion battery cells experience voltage imbalance which leads to capacity mismatch as the cells undergo charge and discharge cycles. An electronic equalizer, EQU, can be used to balance the cell voltages. There are generally two types of equalizers available on the market today: Passive Equalizers (PEQs) and Active Equalizers (AEQs) with each having their advantages and problems. A hybrid equalizer, consisting of both the PEQ and AEQ, developed in previous research [0][1][5][6][7][8][9] called the Bilevel Equalizer (BEQ) solves the weak cell issue at a high efficiency and low cost. It was therefore important to understand how to design, test and simulate the performance of the AEQ units on the BEQ. This research designs and builds the Printed Circuit Board (PCB) for the production model of the 24V and the 48V Efficiency Measuring Apparatus (EMA), previously developed in [1], for low voltage and high voltage applications. The EMA is a device that measures the efficiency of charge transfer between cells or sections of cells. The AEQ Inductor Design Tool and the EQU Design App, both previously developed in Excel and MATLAB respectively in [1], were also developed and implemented in Python in order to design the inductors for the AEQ units and simulate the equalizer performance under different scenarios. Python was selected for this study because it is an open source software that easily accessible to the broader public to disseminate the knowledge and technology. The experimental results from the 24V and the 48V EMA were compared with the expected results from the Python AEQ Inductor Design Tool to validate the models. Furthermore, The BEQ experimental data obtained in [1] was used to validate the Python EQU Design App using the EMA results. In conclusion, the 24V and 48V EMA worked efficiently in predicting the transfer efficiency of the AEQ units of the equalizer. Similarly, the Python Inductor Design Tool yielded an accuracy of 99.59% to 99.99% when compared to the Excel Inductor Design Tool, and an accuracy ranging between 96% and 99.99% in comparison to the EMA experimental results. Likewise, the Python EQU Design App had exact agreement with the MATLAB EQU Design App, and an accuracy ranging between 98.24 % and 98.29% in comparison to the BEQ experimental results from [1]. These results prove that the Python Inductor Design Tool and the Python EQU Design App are adequate tools for predicting the transfer efficiency of EMA AEQ units and the performance of equalizers.
Ngalula Mubenga (Committee Chair)
Thomas Stuart (Committee Member)
Richard Molyet (Committee Member)
102 p.
Electrical Engineering; Engineering
Battery; Lithium-ion; Equalizers; Battery Management Systems; Bilevel Equalizers; Efficiency Measuring Apparatus

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Citations

  • Salami, B. P. (2021). The Efficiency Measuring Apparatus for Li-ion Battery Equalizers [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1619460723390441

    APA Style (7th edition)

  • Salami, Boluwatito. The Efficiency Measuring Apparatus for Li-ion Battery Equalizers. 2021. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1619460723390441.

    MLA Style (8th edition)

  • Salami, Boluwatito. "The Efficiency Measuring Apparatus for Li-ion Battery Equalizers." Master's thesis, University of Toledo, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1619460723390441

    Chicago Manual of Style (17th edition)

toledo1619460723390441
112
© 2021, all rights reserved.
This open access ETD is published by University of Toledo and OhioLINK.