Recursive least squares

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

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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.

Parameterisation

Measuring or fitting the numbers a model needs: OCV curves, diffusion coefficients, exchange current densities, thicknesses and porosities for a physics model, or resistances and time constants for a circuit model. It is usually most of the modelling effort.

RC pair

A resistor and capacitor in parallel, the building block of an equivalent circuit model. Each pair reproduces one time constant of the cell’s voltage response, from the seconds of charge transfer to the minutes of diffusion.

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.

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

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

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.