Unexpected simulation results from BatteryStateful model

Hi there. I would like to use the BatteryStateful model through PySAM. I've managed to set it up, but the results don't seem to be correct.

I have created a minimum working example to show this:

I set up a model with the parameters shown in the attached JSON file ( ). In my Python code, I set the input power of the battery as 10 kW, and the simulation timestep as 1 minute. Then I executed the model. The energy that is supposed to be absorbed by the battery is 10*1/60 = 0.167 kWh. However, the energy that actually gets absorbed into the battery is 2.89 kWh.

I have attached my python code ( ). I also show the code here below, with the output of the script.

The code:

The output:
I hope you can help me figure out what the reason is for this issue. I hope I am not using the model incorrectly...

Chris

Hi Chris,

This behavior seems to be related to the initial state of charge (SOC). In this case, you have set (in the JSON file) the initial SOC to zero (note that the minimum SOC is 20%). I tried changing the initial SOC to 5% and 20%, and for both of those runs, setting 'input_power' to -0.5 resulted in P = -0.5, which I think is the result you are expecting.

For a real battery, you would want to avoid discharging the battery below the minimum SOC.

I'll ask the model developer about this and follow up if there's more to add.

Thanks,
Paul.

Thank you, Paul! I changed my model parameters to ensure that the battery is never fully discharged. However, I have a follow up question: Even when I am operating the battery in the acceptable range, the SOC value does not increase proportionally to the charge added into the battery... My understanding is that the SOC is calculated as the energy in the battery divided by the nominal energy capacity of the battery. However, when I run the model, this does not seem to be the case. For example, if I have a battery with 10 kWh nominal energy, and I start with an SOC of 5%, then I expect 0.5 kWh of energy to be present in the battery. If I then charge the battery with 10 kW over a 1 minute timestep, I expect the 10 * 1/60 = 0.167 kWh of energy to be added to the battery. Thus the energy in the battery would be 0.5 + 0.167 = 0.667 kWh, which results in an SOC of 6.67%. However, when I run this in the model, I get an SOC of 19.3%. I have attached a minimum working example.

Hi Chris,

The leadacid chemistry model uses a "KiBAM" capacity model which accounts for the peukert effect, which is much higher for lead than for li-ion. The model parameters leadacid_q20, leadacid_q10, leadacid_qn, and leadacid_tn are used to fit the leadacid capacity model. From your parameters:

    "leadacid_q20": 100,    "leadacid_q10": 43,    "leadacid_qn": 40,    "leadacid_tn": 1,
Means that at a C rate of 1/20C, the battery has 100% capacity, but at a C rate of 1C ("qn"), the battery only has 40% capacity. These parameters are adjustable, but in practice it is not very flexible in that it is hard to get a set of parameters that can model a wide range of c-rate to capacity behaviors. 

Darice

Thank you for your response, and I am so sorry for my delayed response! What you have said is interesting! This makes me realize the problem with my model parameters. In fact the units of the values are in Ah, not %. When I changed my leadacid_q20 to 833.333 Ah, the results are much closer to what I had expected. Thank you for your help!

Perhaps, it may also be useful to add to the documentation that and are interdependent inputs.