State observer

Example

Use in Lemonergy

Distinguish internal states from noisy measurement estimates.

This is a reference concept. No executable Lemonergy block has been verified for this term.

Connected concepts

Related concepts from the knowledge graph. Connections show context, not cause and effect. Hover or focus a linked term for its definition.

Dual and joint estimation

Two ways to estimate a cell’s fast-changing states, such as SOC, together with its slowly drifting parameters, such as capacity and resistance. Dual estimation runs two coupled filters on different time scales, each using the other’s latest output; joint estimation stacks both into one augmented vector inside a single filter, simpler to structure but larger and harder to tune.

Equivalent circuit model

A cell model built from a voltage source for OCV, a series resistor and one or more RC pairs for the slower dynamics. Its few parameters are fitted from pulse tests, and it is what most battery management systems run in real time.

Kalman filter

A recursive estimator that blends a model prediction with noisy measurements according to how much each is trusted. In a BMS an extended Kalman filter combines coulomb counting with a voltage model to track SOC and sometimes resistance.

Observability and controllability

Two control-theory properties of a model: whether its internal states can be inferred from its measured outputs, and whether its inputs can steer those states. SOC is poorly observable on LFP’s flat OCV plateau, where voltage barely changes with charge, and the individual SOCs of series cells cannot be steered by pack current alone, which is why balancing exists.

Particle filter

A recursive estimator that represents an uncertain state as a cloud of weighted samples, propagating each through the model and reweighting it against every new measurement. Unlike a Kalman filter it copes with strong nonlinearity and non-Gaussian noise, at the cost of hundreds of model runs per step, so it appears more in remaining-useful-life prognostics than in production BMS firmware.

State of charge

How full a cell is, as a fraction of its available capacity. State of charge, like temperature, affects how fast a stored cell ages.

References & further reading

Technical references

1. 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

   Technical reference · Model-based battery state-of-charge and parameter estimation with extended Kalman filtering, including uncertainty and model dependence.

Further reading on this topic

These sources provide broader context; they are not presented as citations for every statement in this entry.

2. 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.