## Generate Parameter Data for Equivalent Circuit Battery Block

Using MathWorks® tools, estimation techniques, and measured lithium-ion or lead acid battery data, you can generate parameters for the Equivalent Circuit Battery block. The Equivalent Circuit Battery block implements a resistor-capacitor (RC) circuit battery with open circuit voltage, series resistance, and 1 through N RC pairs. The number of RC pairs reflects the number of time constants that characterize the battery transients. Typically, the number of RC pairs ranges from 1 through 5.

To create parameter data for the Equivalent Circuit Battery block, follow these workflow steps. The steps use numerical optimization techniques to determine the number of recommended RC pairs, provide initial estimates for the battery model circuit parameters, and estimate parameters to fit a model to experimental pulse discharge data. The results provide the open circuit voltage, series resistance, and RC pair parameter data for the Equivalent Circuit Battery block.

WorkflowDescription

Estimate Equivalent Circuit Lithium-Ion Battery Data

Live script providing workflow steps 1 through 3.

Step 1: Load and Preprocess Data

Load and preprocess time series battery discharge voltage and current data.

Step 2: Determine the Number of RC Pairs

Determine the number of necessary time constants (TC) for estimation.

Step 3: Estimate Parameters

For battery discharge data, estimate and optimize these values:

• Open-circuit voltage, `Em`

• Series resistance, `R0`

• RC pair(s) time constant(s), `Tau`

• RC pair(s) resistance(s), `Rx`

Step 4: Set Equivalent Circuit Battery Block Parameters

Set the Equivalent Circuit Battery block parameters.

## References

[1] Ahmed, Ryan, et al. "Model-Based Parameter Identification of Healthy and Aged Li-ion Batteries for Electric Vehicle Applications." SAE International Journal of Alternative Powertrains. 4, no. 2 (2015): 233 -47. https://doi.org/10.4271/2015-01-0252.

[2] Gazzarri, Javier, Nishant Shrivastava, Robyn Jackey, and Craig Borghesani. "Battery Pack Modeling, Simulation, and Deployment on a Multicore Real Time Target." SAE International Journal of Aerospace. 7, no. 2 (2014): 207–13. https://doi.org/10.4271/2014-01-2217.

[3] Huria, Tarun, Massimo Ceraolo, Javier Gazzarri, and Robyn Jackey. “High Fidelity Electrical Model with Thermal Dependence for Characterization and Simulation of High Power Lithium Battery Cells.” IEEE® International Electric Vehicle Conference, March 2012. https://doi.org/10.1109/ievc.2012.6183271.

[4] Huria, Tarun, Massimo Ceraolo, Javier Gazzarri, and Robyn Jackey. "Simplified Extended Kalman Filter Observer for SOC Estimation of Commercial Power-Oriented LFP Lithium Battery Cells." SAE Technical Paper Series, 2013. https://doi.org/10.4271/2013-01-1544.

[5] Jackey, Robyn A. "A Simple, Effective Lead-Acid Battery Modeling Process for Electrical System Component Selection." SAE Technical Paper Series, 2007. https://doi.org/10.4271/2007-01-0778.

[6] Jackey, Robyn A., Gregory L. Plett, and Martin J. Klein. "Parameterization of a Battery Simulation Model Using Numerical Optimization Methods." SAE Technical Paper Series, 2009. https://doi.org/10.4271/2009-01-1381.

[7] Jackey,Robyn, et al. "Battery Model Parameter Estimation Using a Layered Technique: An Example Using a Lithium Iron Phosphate Cell." SAE Technical Paper Series, 2013. https://doi.org/10.4271/2013-01-1547.

[8] Geng, Zeyang, Jens Groot, and Torbjorn Thiringer. “A Time- and Cost-Effective Method for Entropic Coefficient Determination of a Large Commercial Battery Cell.” IEEE Transactions on Transportation Electrification 6, no. 1 (March 2020): 257–66. https://doi.org/10.1109/TTE.2020.2971454.