Battery knowledge · State estimation
State of health
A summary of how far a cell has aged, often expressed through remaining capacity or increased resistance compared with when it was new. By convention a cell has reached end of life at 80 % of its rated capacity.
- Abbreviation
- SOH
- Unit
- %
Example
Capacity loss and resistance growth
At the illustrated endpoint: 80% initial capacity and 140% initial resistance.
Chosen qualitative trends. The exposure axis is not predicted cycles or years.
Explore the graph
Energy capability and power capability can degrade differently.
At the illustrated endpoint: 80% initial capacity and 140% initial resistance.
Capacity ratio = Q_now/Q_initial; resistance ratio = R_now/R_initial
Illustrative model. Chosen qualitative trends. The exposure axis is not predicted cycles or years.
Use in Lemonergy
Lemonergy Studio blocks
Capacity health index worked example
Endpoint-normalized health index with raw value and out-of-reference flag.
Blocks to use
- Capacity health index
Maps a measured capacity or resistance between user-selected fresh and end-of-life endpoints. It returns a clipped health index, the raw index and a flag for values outside the reference interval.
Inputs & settings
- Use the parameter values in the block reference.
- Bundled example verified on the production release of 28 October 2026.
This overview identifies the components to use. Follow the example in Lemonergy for their connections and settings.
- Review the block inputs, units and model limits.
- Open the Studio example and run its bundled model.
- Compare the output channels with the numerical checks; change one input and run again.
Example. Use the defaults below with Measured capacity (Ah) = 1.8 Ah. At 0 s, Clipped health index is 0.5 1 (fraction). Additional checks are listed below.
Observe. Inspect the outputs and numerical checks on the block reference page.
This endpoint-normalized index is not the same quantity as capacity retention. The endpoint choice determines its interpretation. The example verifies numerical behavior for synthetic parameters. Cell characterization and validation for an intended use are separate tasks.
Open Studio example →Lemonergy Studio blocks
Resistance health index worked example
Endpoint-normalized health index with raw value and out-of-reference flag.
Blocks to use
- Resistance health index
Maps a measured capacity or resistance between user-selected fresh and end-of-life endpoints. It returns a clipped health index, the raw index and a flag for values outside the reference interval.
Inputs & settings
- Use the parameter values in the block reference.
- Bundled example verified on the production release of 28 October 2026.
This overview identifies the components to use. Follow the example in Lemonergy for their connections and settings.
- Review the block inputs, units and model limits.
- Open the Studio example and run its bundled model.
- Compare the output channels with the numerical checks; change one input and run again.
Example. Use the defaults below with Measured resistance (ohm) = 0.075 1. At 0 s, Clipped health index is 0.5 1 (fraction). Additional checks are listed below.
Observe. Inspect the outputs and numerical checks on the block reference page.
This endpoint-normalized index is not the same quantity as capacity retention. The endpoint choice determines its interpretation. The example verifies numerical behavior for synthetic parameters. Cell characterization and validation for an intended use are separate tasks.
Open Studio example →References & further reading
Further reading on this topic
These sources provide broader context; they are not presented as citations for every statement in this entry.
- Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation
Gregory L. Plett · Journal of Power Sources · 2004
Topic background · Model-based battery state-of-charge and parameter estimation with extended Kalman filtering, including uncertainty and model dependence.
- Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification
Gregory L. Plett · Journal of Power Sources · 2004
Topic background · Battery charge balance, open-circuit voltage, ohmic loss, polarization dynamics and model identification; parameters require cell-specific data.