Calendar aging depends strongly on temperature, SOC, and time. This first-pass model combines square-root time growth with Arrhenius temperature dependence.
How to read it
The retention curve shows capacity fade accumulating with storage time.
The aging map highlights high-temperature and high-SOC combinations to avoid.
The breakdown view shows whether temperature, SOC, or time dominates.
Learn Battery Calendar Aging Arrhenius by dialogue
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When reading Battery Calendar Aging Arrhenius, where should I look first? Moving Storage temperature changes both the plots and the result cards.
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Start with Capacity loss, but do not treat the number as the whole answer. Use Capacity retention curve to confirm the assumed state, then read Temperature-SOC aging map for the distribution or trend. The retention curve shows capacity fade accumulating with storage time.
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I can see why Storage temperature changes Capacity loss. How should I judge the influence of Storage SOC?
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Move Storage SOC in small steps and watch Remaining capacity. That reveals which term is controlling the result. Calendar aging depends strongly on temperature, SOC, and time. This first-pass model combines square-root time growth with Arrhenius temperature dependence. A single operating point is not enough; sweep the realistic scatter range.
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What is Aging factor breakdown for? It feels like the ordinary curve already tells the story.
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Aging factor breakdown is for finding boundaries where the condition becomes risky or margin collapses quickly. The aging map highlights high-temperature and high-SOC combinations to avoid. In Estimating fade during warehouse storage or transport, the important question is often what happens after a small change, not only the nominal value.
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So if Capacity loss is within the target, can I accept the condition?
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Treat this as a first-pass review. It helps with Planning retest timing for long-held inventory and Comparing storage SOC and temperature-control policies, but final decisions still need standards, measured data, detailed analysis, and vendor limits. The breakdown view shows whether temperature, SOC, or time dominates.
Practical use
Estimating fade during warehouse storage or transport.
Planning retest timing for long-held inventory.
Comparing storage SOC and temperature-control policies.
FAQ
Start with Capacity loss and Remaining capacity. Then use Capacity retention curve to confirm the assumed state and Temperature-SOC aging map to read distribution or bias. The retention curve shows capacity fade accumulating with storage time
Move Storage temperature alone, then move Storage SOC by a comparable amount and compare the change in Capacity loss. Aging factor breakdown shows combinations where margin or performance changes quickly.
Use it for Estimating fade during warehouse storage or transport. Instead of trusting a single point, widen the input range and check whether Capacity loss keeps enough margin before moving to detailed analysis.
Calendar aging depends strongly on temperature, SOC, and time. This first-pass model combines square-root time growth with Arrhenius temperature dependence. Final decisions still require standards, measured data, detailed analysis, and vendor limits.
How to Use
Enter storage temperature (°C) in the storageTempVal field—typical range 0–55°C for lithium-ion cells
Set state of charge (%) in socVal; higher SOC (80–100%) accelerates fade versus 50% nominal storage
Input storage duration in monthsVal and reference fade rate (% per year) from your cell datasheet, typically 2–5% annually at 25°C and 50% SOC
Specify activation energy (eV) if known from manufacturer specifications; 0.4–0.6 eV is standard for lithium-ion calendar aging
The simulator applies Arrhenius equation to calculate acceleration factor and projects remaining capacity percentage
Worked Example
A LiFePO4 battery stored at 40°C and 90% SOC for 18 months with base fade of 3% per year (measured at 25°C, 50% SOC) and activation energy 0.52 eV: Acceleration factor = exp[0.52 × (1/298 − 1/313) / 0.0000862] ≈ 2.1× at 40°C. Calendar loss over 18 months = 3% × (1.5 years) × 2.1 + SOC penalty ≈ 11.2% capacity loss. Remaining capacity: 88.8%. At 25°C reference, equivalent storage time would be 3.15 years to achieve same fade.
Practical Notes
SOC significantly impacts fade—store automotive packs at 40–60% SOC and avoid sustained 90%+ storage unless accepting 3–5× aging acceleration
Temperature sensitivity dominates: each 10°C rise roughly doubles fade rate for lithium chemistry; cold storage (5–15°C) extends calendar life by 40–60%
Activation energy varies by chemistry—LFP (~0.45 eV) ages slower than NCA/NMC (~0.55–0.65 eV); verify from cell test reports
Combine calendar fade with cycle fade estimates when modeling total capacity loss in stationary storage or grid applications
