Battery storage sizing fails when a design treats nameplate capacity and cycle count as enough to predict how much energy and power a battery will deliver years later. A defensible LiFePO₄ model must account for calendar time as well as cycling, use operating conditions such as temperature and depth of discharge, and track capacity fade separately from resistance growth.
In TypeScript, the important first step is not choosing a clever equation: it is making the model’s cell, test conditions, units, parameters, and validation range explicit. Published aging results are useful evidence, not transferable guarantees for another cell or implementation.
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How do I size a battery for storage?
Start with the energy and power the application must deliver, then test whether the battery can still meet both requirements at the end of the design horizon. Beginning-of-life nameplate energy is not the same as energy available at a particular state-of-charge (SOC) window, temperature, discharge rate, or state of health (SOH).
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If the load needs a specified amount of delivered energy, a first-pass relationship is:
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Required beginning-of-life nominal energy ≥ required delivered energy ÷ (usable SOC fraction × discharge efficiency × end-of-life capacity fraction).
This is a screening relationship, not a complete battery design. The usable SOC fraction is the portion of the battery’s capacity the control strategy permits the application to use; the end-of-life capacity fraction must come from an aging assumption or model appropriate to the cell and operating conditions. Account for additional system losses and reserve requirements if they apply to the installation.
Check power separately from energy
A battery may retain enough capacity on paper but fail a power requirement. Increasing internal resistance can increase voltage sag under load: in a simplified electrical model, terminal voltage under discharge is open-circuit voltage minus current multiplied by resistance. The actual voltage response depends on the cell and operating conditions, so resistance growth should be treated as a distinct modeled output, not folded into a capacity-fade percentage.
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Run the sizing calculation across the expected duty cycle, including discharge rate, SOC window, temperature, storage intervals, and design life. The study Sizing of Battery Energy Storage Systems for Firming PV Power including Aging Analysis treats aging as part of storage sizing; its authorship and publication metadata are not stated here, so no broader attribution is warranted.
Why can cycle count mislead a LiFePO₄ lifetime estimate?
Cycle count does not describe how hard the cell was cycled, how long it spent at a given SOC, or the temperature at which it operated. Published LFP aging studies vary temperature, charge and discharge rate, depth of discharge (DOD), and SOC conditions because those factors affect degradation.
Temperature, rate, and depth of discharge interact
The 2011 study Cycle-life model for graphite-LiFePO₄ cells tested temperatures from −30 to 60 °C, DOD values from 90% to 10%, and rates from C/2 to 10C. It reported that at low rates, capacity loss was strongly affected by time and temperature, while DOD was less important; at high rates, charge and discharge rate effects became significant. Those findings describe that study’s cells and test matrix, not every LFP product.
The practical consequence is that “number of cycles” needs context. A cycle-aging model should use a defined exposure measure—such as charge throughput or cycles with specified depth and SOC range—and should not silently treat shallow cycling as equivalent to deep cycling.
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Capacity and resistance can change while a battery is stored, even without cycling. The 2020 paper Analysis and modeling of cycle aging of a commercial LiFePO₄/graphite cell analyzed 19 cycle-aging and 17 calendar-aging test points over 885 days, varying temperature, C-rate, depth of cycle, and SOC range. Its combined model represents calendar and cycle contributions to both capacity loss and resistance increase. Keep elapsed storage time and cycling exposure as separate inputs unless a calibrated model explicitly combines them.
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How does LiFePO₄ battery capacity degrade over cycles?
Capacity fade is a change in how much charge or energy a cell can deliver under a specified measurement procedure. It is not identical to resistance growth, and it cannot be inferred reliably from cycle count alone. A practical empirical or semi-empirical model estimates the change from the cell’s operating history and calibrated parameters.
Use a model family that matches the evidence
Published approaches include empirical cycle-life relationships, semi-empirical models that combine observed aging behavior with mechanism-informed structure, and more detailed electrochemical models. There is no universal winner established by the cited studies. Choose based on the prediction you need, the data available, and whether the model’s cell and test range match your application.
| Approach | What it can represent | What to verify |
|---|---|---|
| Empirical cycle-life model | Observed capacity change against a defined cycling or throughput measure, potentially with fitted temperature scaling. | Whether its cell, temperature, rate, DOD/SOC, and test-duration range covers the intended use. |
| Semi-empirical model | Separate or combined calendar and cycling contributions, with outputs such as capacity loss and resistance increase. | How each contribution is defined, how the parameters were fitted, and whether outputs were validated against relevant aging data. |
| Electrochemical or physics-based model | More detailed representation of cell behavior when the required inputs and parameterization are available. | Whether the model is actually parameterized and validated for the target cell and operating conditions; detail alone does not guarantee accuracy. |
For every model, define the capacity measurement method and baseline. “Capacity” should refer to a stated test procedure, not a value that changes implicitly between the fitted data and the sizing calculation.
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How does temperature affect LiFePO₄ battery life?
Temperature affects aging, but an ambient-temperature reading may not represent the temperature inside a module. If a model uses cell or module temperature, state whether that value was measured, estimated, or approximated from ambient conditions.
Arrhenius scaling requires fitted parameters
A common empirical temperature multiplier for a rate is written as f(T) = exp[(Ea/R) × (1/Tref − 1/T)], where T and reference temperature Tref are in kelvin, R is the gas constant, and Ea is a fitted activation parameter. In this convention, a positive activation energy makes the multiplier increase with temperature. The equation is a temperature-scaling form, not a universal LFP law: the fitted parameter, temperature convention, and reference conditions must come from the model being implemented. Do not substitute guessed activation energies.
Jung, Kim, Park, Kim, and Kim’s 2021 study, Cycle-life prediction model of lithium iron phosphate-based lithium-ion battery module, tested an eight-cell module with eight thermocouples. It compared ambient, external, internal, and total-average module temperatures in Arrhenius-based models; the total-average-temperature model had the lowest average percentage error in that experiment. That result supports attention to module temperature measurement, not a guarantee that total average temperature will be best for every pack.
Why model internal resistance separately from capacity?
Capacity loss describes reduced charge or energy delivery under a defined test; resistance increase describes a change in electrical response. Both can affect storage performance, but in different ways. Higher resistance can worsen voltage sag and limit power before a capacity-based energy estimate reaches its nominal limit.
The 2020 commercial-cell study modeled both outputs and evaluated them under two dynamic load profiles. It reported errors below 1% for capacity loss and below 2% for resistance increase for those profiles. Those are study-specific validation results, not expected error bounds for another cell, model fit, or TypeScript implementation.
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Define what “resistance” means in the model—such as a particular measured resistance or a named proxy—and preserve the same measurement method across calibration and validation. Do not add an unverified universal resistance-growth law: the cited evidence does not establish one transferable law for all LFP cells.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to structure an aging model in TypeScript
No validated TypeScript package or universal parameter set is established by the cited sources. Treat the code below as an engineering structure for connecting calibrated model functions, not as a published or ready-to-use LFP aging law. Supply the functions from a model calibrated to the cell and conditions you intend to simulate.
Make units, provenance, and exposure explicit
- Identify the cell or module, its measured initial capacity, and the reference capacity and resistance test methods.
- Record charge throughput or another defined cycling measure, DOD and SOC window, charge and discharge rate, cell or module temperature, calendar time, and storage SOC.
- Store every fitted parameter with its units, source, calibration cell, and valid test range.
- Use kelvin inside temperature equations, converting Celsius once at the input boundary and rejecting invalid temperatures.
Keep capacity and resistance trajectories separate
This example expects calibrated functions to return incremental changes for a particular aging step. Their signatures make units explicit; their mathematical forms and parameters must be supplied and tested for the selected cell.
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capacityAh: number;
resistanceOhm: number;
elapsedDays: number;
throughputAh: number;
};
type Exposure = {
elapsedDays: number;
throughputAh: number;
cellTemperatureK: number;
meanSoc: number;
dodFraction: number;
chargeRateC: number;
dischargeRateC: number;
};
type AgingModel = {
capacityLossAh: (state: State, exposure: Exposure) => number;
resistanceIncreaseOhm: (state: State, exposure: Exposure) => number;
};
function advance(state: State, x: Exposure, model: AgingModel): State {
if (x.elapsedDays < 0 || x.throughputAh < 0) {
throw new Error("Exposure increments must be non-negative");
}
if (x.cellTemperatureK <= 0) {
throw new Error("Temperature must be greater than zero kelvin");
}
const capacityLoss = model.capacityLossAh(state, x);
const resistanceIncrease = model.resistanceIncreaseOhm(state, x);
if (capacityLoss < 0 || resistanceIncrease < 0) {
throw new Error("Aging increments must be non-negative");
}
const next: State = {
capacityAh: state.capacityAh - capacityLoss,
resistanceOhm: state.resistanceOhm + resistanceIncrease,
elapsedDays: state.elapsedDays + x.elapsedDays,
throughputAh: state.throughputAh + x.throughputAh,
};
if (next.capacityAh <= 0 || next.resistanceOhm <= 0) {
throw new Error("Model produced a non-physical state");
}
return next;
}Passing current state into each increment function allows a calibrated model to account for state-dependent behavior, but it does not guarantee that any chosen function is valid. If calendar and cycle contributions are modeled separately, make their definitions explicit and ensure they are not double-counted. Keep the resistance output’s units and measurement proxy consistent with the model’s calibration.
Use Arrhenius scaling only with its calibrated convention
A temperature helper can implement the common rate-multiplier convention shown above. It must not be used unless the activation parameter and reference temperature have been fitted for the aging process and compatible cell data.
const R_J_PER_MOL_K = 8.314462618;
function arrheniusRateMultiplier(
temperatureK: number,
referenceTemperatureK: number,
activationEnergyJPerMol: number,
): number {
if (temperatureK <= 0 || referenceTemperatureK <= 0) {
throw new Error("Temperatures must be in kelvin and greater than zero");
}
if (activationEnergyJPerMol < 0) {
throw new Error("Use a fitted parameter and its documented convention");
}
return Math.exp(
(activationEnergyJPerMol / R_J_PER_MOL_K) *
(1 / referenceTemperatureK - 1 / temperatureK),
);
}The non-negative check reflects the convention in this example, not a universal rule for every fitted model. If the source model defines its parameter or multiplier differently, implement that documented convention instead.
How should a TypeScript aging model be validated?
Validate model outputs against measured or published time series for the same cell and sufficiently similar conditions. Check both capacity and resistance where both matter to the sizing decision; agreement in capacity alone does not validate power behavior.
Match the validation conditions
- Compare predictions at the same time points or throughput levels and use the same capacity and resistance test definitions as the reference data.
- Check temperature, rate, DOD/SOC, and duty profile against the calibration and validation ranges; flag rather than conceal extrapolation.
- Test zero cycling with elapsed time, and cycling with zero elapsed time, so calendar and cycle effects are not accidentally conflated.
- Check units, temperature conversion, monotonicity where the fitted model requires it, and behavior at the boundaries of the supported range.
- Report model version, parameter provenance, calibration cell, validation conditions, and uncertainty alongside predicted usable energy and power.
For example, Nan et al. (2026) report a segmented model evaluated on 280 Ah cells: prediction to 880 days, equivalent to 3,750 cycles, using 90 days of accelerated-aging data plus 70 days of normal-aging data, with endpoint prediction error below 4% at SOH below 0.87. These are outcomes under that paper’s specified protocol, not an accuracy claim for a different battery or software model. The 2017 paper Comprehensive modeling of temperature-dependent degradation mechanisms in lithium iron phosphate batteries also describes separating calendar and multiple cycle-aging effects and validation using a dynamic current profile associated with stationary storage.
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