Battery state of health (SOH) assessment using KVI's battery analyser BA-2000

• Main Author: Tey, Bryan Jun Hong
• Other Authors: Tang Xiaohong
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2022
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/157619
• Institution: Nanyang Technological University

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.