With increasing global attention on environmental issues and the greenhouse
effect, electric vehicles (EVs) have become a focal point for sustainable
transportation solutions. Lithium-ion batteries are integral to EVs due to their
high energy density, elevated operating voltage, and long service life. However,
their performance is highly influenced by factors such as ambient temperature,
charge and discharge rates, and aging processes. To enhance the safety,
reliability, and efficiency of lithium-ion battery systems, it is critical to
develop a robust and advanced battery management system (BMS) that can monitor
battery states accurately and in real-time. A key aspect of BMS design is the
estimation and prediction of the battery's state of health (SOH). Accurately
characterizing SOH during actual usage conditions is essential for optimal
battery performance and longevity. This study investigates various SOH indicator
extraction methods reported in the literature, including features related to
voltage, temperature, and incremental capacity (IC) curves. Correlation analyses
of these indicators are conducted using two experimental datasets. The results
demonstrate that the selected indicators have strong correlations with battery
SOH, confirming their effectiveness for SOH estimation and prediction. By
validating the effectiveness of specific SOH indicators, this research
contributes to the development of more reliable and efficient battery management
strategies, offering significant practical application value in the advancement
of electric vehicle technologies.