Battery state of health (SOH) assessment using KVI's battery analyser BA-2000
In the field of energy storage systems, batteries are often used in numerous daily applications. One of the most common yet important issue is the evaluation of the battery lifespan so that its cells can be better optimized, and actions can be taken to either mitigate the degradation or replac...
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| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2022
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/157619 |
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| 總結: | In the field of energy storage systems, batteries are often used in numerous daily
applications. One of the most common yet important issue is the evaluation of the battery
lifespan so that its cells can be better optimized, and actions can be taken to either mitigate
the degradation or replace it altogether. There are several methods to obtain this piece of
information and this report aims to evaluate the advantages of using thermodynamics over
complex algorithms and data analytic models.
In this paper, an assessment of a battery’s State of Health (SOH) is carried out using KVI’s
BA2000 battery analyzer module which extracts key parameters such as Enthalpy and
Entropy data that are subsequently used to determine the relativity to its original State of
Health. The battery analyzer system utilizes a combination of Constant Current Constant
Voltage (CCCV) and Electrochemical Thermodynamic Measurements (ETM) protocols to
obtain the measurements. The primary usage of this module is to define several charge
states where thermodynamic properties will be measured as well as the temperature range
over which they will be calculated.
Other modules such as the Chentech Power Cell Module will also be used in conjunction
with the BA2000 to minimize the experiment duration and to provide a more holistic
analysis of the battery’s charging characteristics. This experiment will primarily focus on
extracting the thermodynamic data measurements and plotting them against other critical
data to obtain the relationship between thermodynamic parameters, State of Charge and
State of Health with the use of Excel Simulations.
The results from this experiment indicate the accessibility to SOH data without the need
of complex algorithms or extremely robust software and shows the correlation between
thermodynamic parameters and State of Health i.e., Profile Analysis. |
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