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This blog provides a sneak peek of our latest pre-print (Figgener et al.) on estimating degradation modes in battery systems, building on our previous work on capacity estimation published in Nature Energy. This extended analysis draws from a robust field dataset, including data from 21 home storage systems tracked over eight years to develop a method to estimate open circuit voltage (OCV) curves and analyze capacity loss. The approach not only shows that capacity fades but also gives an answer on why.
Open Circuit Voltage (OCV) curves are fundamental in understanding a battery’s electrochemical profile, showing the relationship between the state of charge (SOC) and cell voltage in an open-circuit state (i.e. when the battery is not charged or discharged). As batteries age, changes in active material composition and cell balancing alter the OCV curve, making it a critical indicator for tracking degradation modes over time.
Analytical techniques such as Incremental Capacity Analysis (ICA) and Differential Voltage Analysis (DVA) further enhance OCV-based assessments. ICA involves analyzing battery capacity with respect to voltage, highlighting identifiable peaks that correlate with changes in electrochemical properties. These peaks allow for detailed monitoring of aging processes and provide insights into specific degradation modes. DVA, in addition, involves investigating voltage with respect to capacity, with peaks indicating transitions between voltage plateaus. This technique helps track the loss of active lithium as well as electrode degradation and shifts in balancing between the cathode and anode, offering a nuanced view of battery health.
Directly measuring OCV requires the battery to be in a relaxed state to eliminate any overvoltage due to different resistance components such as ohmic and diffusion resistances, which limit accurate OCV assessments. Standard methods, like low-current and incremental OCV tests, demand high precision in measuring equipment and stable temperature control, mostly conducted in laboratories.
Additionally, ICA and DVA techniques are commonly applied in lab settings to interpret OCV curves, but field applications are challenging due to measurement noise and variability in real-world conditions. This dependency on controlled environments makes OCV-based aging analysis, as well as ICA and DVA, challenging to replicate outside of controlled lab conditions, limiting their accessibility and usefulness for real-world battery monitoring.
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Our approach offers a way to reconstruct OCV curves directly from field data, allowing for practical battery health monitoring and aging analysis in real-world conditions. By using existing operational data from home storage systems, we transform routine voltage and SOC measurements into accurate OCV curves.
Thus, we enable battery system operators and owners to assess battery health in real-time. With the refined OCV curve, key features of interest (FOIs) identified can be selected and analyzed. These FOIs include specific peak positions and intensities, offering valuable insights into battery health and degradation. Monitoring the FOIs enables a comprehensive understanding of occurring degradation modes, allowing for more accurate predictions of long-term battery performance. Figure 1 shows the OCV, ICA, and DVA results for an LMO/NMC system over seven years.
Figure 1: Analysis of an LMO/NMC blend: Adapted from Figgener et al. 2024, used under CC BY 4.0.
A detailed FOI discussion is presented in our comprehensive publication. Here are the key takeaways:
Loss of lithium inventory is dominant. Most of these FOIs point to Loss of Lithium Inventory (LLI) as the dominant degradation mode. LLI reflects a gradual reduction in the amount of lithium available to participate in the electrochemical reactions within the battery. This mode is a primary driver of capacity loss, as lithium ions become inactive over time due to side reactions and the formation of interphases. These interphases effectively trap lithium ions, reducing the battery's total available capacity and leading to observable declines in capacity.
Loss of active material is neglectable. While LLI is the predominant factor identified in most FOIs, some FOIs also suggest the occurrence of Loss of Active Material (LAM) within the electrodes. LAM is indicative of structural degradation in the electrode materials, where sections of active material become electrically isolated or chemically degraded, thus losing their ability to contribute to the cell's overall charge and discharge processes. Although LAM contributes to degradation, its impact on capacity loss is relatively minor compared to LLI for the system analyzed.
By reconstructing OCV curves from operational field data and applying analytical techniques like ICA and DVA, our approach brings insights traditionally limited to controlled lab settings into practical, real-world applications.
Our analysis allows for a comprehensive understanding of degradation modes, and enables accurate, ongoing monitoring of battery health, providing operators and owners with actionable information that can be used to enhance performance, extend battery life, and refine management strategies based on real-world behavior. Using techniques like incremental capacity analysis (ICA) and differential voltage analysis (DVA), we can provide more valuable into battery aging, enabling better lifetime predictions and enhanced safety analytics.
Dr. Georg Angenendt: The Fingerprint of the Battery: Understanding Open-Circuit Voltage
Dr. Jan Figgener et al.: Degradation mode estimation using reconstructed open circuit voltage curves from multi-year home storage field data
Dr. Jan Figgener et al.: Multi-year field measurements of home storage systems and their use in capacity estimation
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Matthias helps customers gain actionable insights into their battery systems. As Senior Battery Expert and leader of the battery expert team at ACCURE he is responsible for the development of leading-edge battery diagnostics and analytics. As an accomplished systems engineer, Matthias has extensive experience in automotive battery system development. He holds a Master of Science in Electrical Engineering, Information Technology and Technical Computer Science. In his free time, he enjoys sports including soccer, skiing, snowboarding, and jogging.