Temperature in Kelvin and Celsius for LMO/LTO model in BatteryStateful

Dear Paul and the rest of the group

When using the default LMOLTO model I get strange results on the temperature. When reading the docs I realize that there might be a misunderstanding in terms of unit. All other temperatures are given in Celsius while "temp_avg" (only used for the LMO/LTO model) are given in Kelvin. 

The battery I simulate runs for 70,000 cycles, I believe thats too much. In addition I get strange cyclic values on the temp_avg, see table below. 

Could you possibly send me a piece of code in Python on how to correctly setup and initiate and size and run a LMO/LTO battery that will simulate according to expectations? Or, please explain how to do it in order to not get such strange behavior. I could of course offer you my code as well. 

BR
Pontusdatetimetemp_avg - Average temperature for current day [K]n_cyclesday_age_of_batteryT_batt - Battery temperature averaged over time step [C]T_room2023-02-16 13:20281,0172983,00157459,9620,0420,002023-02-16 13:00268,8072983,00157459,9220,0420,002023-02-16 12:40256,5872983,00157459,8720,0420,002023-02-16 12:20244,3672983,00157459,8320,0420,002023-02-16 12:00232,1572983,00157459,7920,0520,002023-02-16 11:40219,9372983,00157459,7520,0520,002023-02-16 11:20207,7172983,00157459,7120,0520,002023-02-16 11:00195,5072983,00157459,6720,0620,002023-02-16 10:40183,2872983,00157459,6220,0620,002023-02-16 10:20171,0672983,00157459,5820,0620,002023-02-16 10:00158,8572983,00157459,5420,0720,002023-02-16 09:40146,6372983,00157459,5020,0720,002023-02-16 09:20134,4172983,00157459,4620,0820,002023-02-16 09:00122,1972983,00157459,4220,0820,002023-02-16 08:40109,9772983,00157459,3720,0920,002023-02-16 08:2097,7672983,00157459,3320,0920,002023-02-16 08:0085,5472983,00157459,2920,1020,002023-02-16 07:4073,3272983,00157459,2520,1120,002023-02-16 07:2061,1072983,00157459,2120,1120,002023-02-16 07:0048,8872983,00157459,1720,1220,002023-02-16 06:4036,6672983,00157459,1220,1320,002023-02-16 06:2024,4472983,00157459,0820,1420,002023-02-16 06:0012,2272983,00157459,0420,1520,002023-02-16 05:400,0072983,00157459,0020,1620,002023-02-16 05:20281,0172983,00157458,9620,1720,002023-02-16 05:00268,7872983,00157458,9220,1820,002023-02-16 04:40256,5672983,00157458,8720,1920,002023-02-16 04:20244,3472983,00157458,8320,2020,002023-02-16 04:00232,1272983,00157458,7920,2220,002023-02-16 03:40219,8972983,00157458,7520,2320,002023-02-16 03:20207,6772983,00157458,7120,2520,002023-02-16 03:00195,4472983,00157458,6720,1320,002023-02-16 02:40183,2272982,00157458,6220,0120,002023-02-16 02:20171,0172982,00157458,5820,0120,002023-02-16 02:00158,7972982,00157458,5420,0120,00

Hello Pontus,

The data you pasted is hard to parse. Are you able to send a CSV with the data?

Thanks,
Darice

Dear Darice

Thank you for you swift reply. Attached is a csv file with the data. N.B. I only took out some of the data which I judged to be important. 
May I suggest a quick way forward if the data is not sufficient: Please return with a small script on a correct setup and simulation of the LMO/LTO battery. What variables do you need to assign, what helper functions do you need to use, how to control the battery according to a power signal. 

BR Pontus

Hi Pontus,

Try this:

import PySAM.BatteryStateful as battstfl
import PySAM.BatteryTools as BatteryTools
b = battstfl.default("LFPGraphite")
BatteryTools.battery_model_sizing(b, desired_power=2.5, desired_capacity=10, desired_voltage=240, size_by_ac_not_dc=True, tol=0.2)
params = {
# specify power input
"control_mode": 1, "input_power": 0, "dt_hr": 1,
"initial_SOC": 50.000, "maximum_SOC": 95.000, "minimum_SOC": 5.000,
}
for k, v in params.items():
b.value(k, v)
b.setup()
b.execute()

Best,
Darice

Dear Darice

Thanks a lot. It goes in line with what I expect. Still I dont get my hands around the issue yet.
N.B. I see that the battery used is LFPGraphite and not LMOLTO. Could you update the code snippet with the correct chemistry and eventual additional settings needed for the LMOLTO battery?

I would like to ask for the rest of the code as well for simulating a battery over its full lifetime.
I.e. what comes after the
"
import PySAM.BatteryStateful as battstfl
import PySAM.BatteryTools as BatteryTools
b = battstfl.default("LFPGraphite")
BatteryTools.battery_model_sizing(b, desired_power=2.5, desired_capacity=10, desired_voltage=240, size_by_ac_not_dc=True, tol=0.2)
params = {
# specify power input
"control_mode": 1, "input_power": 0, "dt_hr": 1,
"initial_SOC": 50.000, "maximum_SOC": 95.000, "minimum_SOC": 5.000,
}for k, v in params.items():
b.value(k, v)
b.setup()
b.execute()
"?

Is it something like:
power_signal = [a list with many power signals, e.g. repeated charge and discharge]

for power_value in power_signal:
        # Set the power level you want
        b.Controls.input_power = power_value 
    
        # Execute the simulation
        b.execute()
        
        # Save the values
        """Here I create a df and "exports" the interesting variables like:
        'SOC_ending': b.StatePack.SOC,
        'SOC_ending': b.StateCell.temp_avg,
        """

        loop until replacement_capacity < e.g. 80 %

N.B. Maybe this editing tool is not good with tabs so I will attach a file as well.


Best Regards Pontus

 

Hi Pontus,

The LMO/LTO life model in SAM is out of date and tends to degrade slower than expected. I have shared an example of using a custom battery life model with BatteryStateful in PySAM here:  github.com/NREL/pysam/blob/main/Examples/BatteryStateful_CustomLifeModel.ipynb

The LMO/LTO life model there is up to date and the reference for it is in that notebook.

Best,
Darice