Contra: estimate parameters separately, for example:

• Quick way to estimate diffusion resistance and capacitance in equivalent-circuit model of a cell
• Estimating cell capacity using linear regression with state-of-charge

Estimating all Cell parameters at once is ultimately more robust because Li-ion cells have just one output signal: voltage.

The changes to the voltage response can often be attributed to changes in different cell parameters (e. g., self-discharge rate, or the open-circuit voltage relationship, or charge capacity). Estimating all parameters in separation (e. g. using linear regression) will overreact to the changes in the cell by fully attributing them to several different parameters. Moreover, the variances of the estimates of the different parameters will not be coherent.

Ways to estimate cell parameters at once:

• Using Stochastic estimator of cell parameters based on an electrical cell model
• Estimator of cell parameters based on particle swarm optimisation

Via:

• The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety
• Digital twin for battery systems: Cloud BMS with online SoC and SoH estimation

References

Online capacity estimation of lithium-ion batteries with deep long short-term memory networks The main limitation of [Kalman Filter or linear regression] approaches is that their accuracy depends highly on the choice of underlying battery model. Moreover, the identification and tuning of the parameters of the model so that it captures all the underlying cell degradation mechanisms is a challenging and often computationally expensive task, complicated by the requirement to simultaneously estimate state of charge.