Batteries, like all engineered systems, don’t last forever. Over time, they gradually lose capacity, efficiency, and power output, a process known as battery degradation or aging. This decline in performance affects everything from electric vehicle range to smartphone battery life. In our previous post, we explored how heat generation and thermal models play a central role in battery performance. One of the most significant consequences of elevated temperature is its impact on battery degradation, but temperature is just one piece of the puzzle.
Cycling aging vs calendar aging
Battery degradation occurs due to a combination of physical and chemical changes within the cell. Repeated charge and discharge cycles, extreme operating temperatures, and even just the passage of time all contribute to the aging process. Typically, aging is classified into two categories: cycling aging and calendar aging. Cycling aging refers to degradation caused by the battery’s operation, charging and discharging over time. Calendar aging, on the other hand, occurs simply due to the passage of time, even if the battery is sitting on a shelf unused. Some key degradation mechanisms include the growth of the solid electrolyte interphase (SEI), lithium plating, and loss of active material. While we’ll explore these mechanisms in detail in an upcoming post, what matters practically is how we measure and quantify battery health.
Defining state of health
This is where State of Health (SoH) comes in. SoH is a metric that quantifies how much a battery has degraded compared to its original state. It is typically expressed as a percentage, with 100% representing a brand-new battery and lower values indicating reduced capacity or performance.
How SoH is measured
There are different ways to define and measure SoH, depending on the application:
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Capacity-based SoH: The most common definition, this measures how much of the original capacity remains. For example, if a battery was originally rated for 100 Ah and now delivers only 80 Ah, its SoH is 80%. This is measured under specific discharge conditions, and different conditions may yield different SoH measurements.
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Resistance-based SoH: Since internal resistance increases with aging, some methods estimate SoH by tracking the rise in resistance over time. Higher resistance leads to greater energy losses and reduced power output.
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Model-based SoH estimation: Advanced battery management systems (BMS) use a combination of real-time data, historical usage patterns, and electrochemical models to estimate SoH dynamically.
Why SoH matters in practice
Monitoring SoH is essential for ensuring reliable battery operation. In consumer electronics, a lower SoH means shorter battery life. In electric vehicles, it affects driving range and charging speed. In grid storage, it influences energy availability and system efficiency. Knowing when a battery has reached an unacceptable SoH threshold helps determine when it should be replaced, repurposed, or recycled.
Understanding battery degradation is key to extending battery lifespan, improving reliability, and designing better energy storage systems.
SoH estimation from raw cycler data is a prediction task that requires physics-based models, especially when tracking how parameters shift with aging. Ionworks Studio’s Predict stage automates SoH tracking across the cell lifetime, mapping measured degradation onto model parameters. Book a demo to see aging prediction in action.
In our next post, we’ll take a closer look at the mechanisms behind battery aging, the microscopic processes that drive capacity fade and power loss over time.
Frequently asked questions
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