Battery knowledge · State estimation
OCV–SOC curve
The characteristic relation between a cell’s open-circuit voltage and its state of charge, measured once at low current and stored in the BMS. A steep curve, as for NMC, makes SOC easy to read from voltage; LFP’s flat curve does not. [1]
Example
Voltage under load
5 A through 20 mΩ gives 0.1 V ohmic drop.
Chosen OCV curve; no chemistry calibration, hysteresis or voltage cutoff.
Explore the graph
Discharge current creates an ohmic drop below open-circuit voltage.
5 A through 20 mΩ gives 0.1 V ohmic drop.
V_terminal = OCV − I_discharge R
Illustrative model. Chosen OCV curve; no chemistry calibration, hysteresis or voltage cutoff.
Use in Lemonergy
Lemonergy Studio blocks
Connect SOC and voltage
The battery integrates charge and combines OCV with resistance.
Blocks to use
- Battery with SOC-dependent OCV
Stores charge and supplies terminal voltage
Inputs & settings
- capacityAh
- initialSoc
- emptyVoltageV / fullVoltageV
- resistanceOhm
- Electrical ports p and n
- Regulated CC/CV charger
Regulates current and voltage
Inputs & settings
- currentA
- voltageV
- cutoffCurrentA / cutoffHoldS
- Electrical ports p and n
- Electrical reference
Sets zero potential
Inputs & settings
- Connect p to reference net
This overview identifies the components to use. Follow the example in Lemonergy for their connections and settings.
- Inspect capacity, initial SOC and OCV endpoints.
- Compare OCV with terminal voltage.
Example. OCV endpoints of 300 and 420 V give 360 V at 50% SOC. Charging at 10 A through 0.08 Ω adds 0.8 V.
Observe. At unity coulombic efficiency, 10 A for 1 h adds ten percentage points to 100 Ah.
Internal model SOC is not an estimate of an unknown physical battery. OCV is linear and resistance fixed.
Open the Studio example →References & further reading
Technical references
- 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
Technical reference · Battery charge balance, open-circuit voltage, ohmic loss, polarization dynamics and model identification; parameters require cell-specific data.
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.