BatteryStateful ageing

When modeling BatteryStateful in Python focusing on battery ageing there is a detail that I do not manage to figure out. Following up on yesterday's discussion on the SAM round the table meeting, I did find the sentence I mentioned. It says "Thermal effects on battery lifetime are not considered." See the document named "Technoeconomic Modeling of Battery Energy Storage in SAM." in the Battery publications.

From what I understand in the "Battery Cell and System" in the Windows-installed version of SAM you can enter a curve/table for how the capacity fades during certain temperatures. However, this effect lasts only momentarily, and the capacity returns to normal when the battery has cooled (if we set aside all other life dependencies), and it is presented in the StateCell.q_relative_thermal? This means there is no permanent effect on the capacity of the battery? To the best of my knowledge, running the battery with rapid charge/discharge will increase its temperature. This increase will in one way or another have a permanent negative effect on the capacity.

Further, if the battery is located outside with ambient temperature profile, there will be no effect on battery life other than capacity fading? Capacity decreases with decreasing temperature generally, and vice versa.

I am using the default('LFPGraphite') model, I also apply the helper function size_batterystateful. 

Thankful for your support.

Hi Pontus,

The LFP/Graphite battery does not have an algebraic model for battery life, so you’ll have to use the calendar cycling model for that battery type. The calendar cycling model does not account for thermal effects on battery life. In actual batteries, temperature has both permanent and transient capacity effects on battery life due, but unfortunately only the LMO/LTO and NMC/Gr models in SAM include those effects.

For the battery life model in BatteryStateful, the state variables are carried over, and so at any time step there is information about how many cycles have elapsed, intermediate variables for tracking cycles via the rainflow algorithm, and recent temperatures of the battery, etc. For the algebraic life models, they are “integrated” at the end of every day: during the day BatteryStateful will have state variables about the elapsed SOC, temps, etc of that day and at the end of the day, those values are integrated into a capacity loss. It’s really the same thing as for the normal battery model, except in BatteryStateful, you get all the state variables so you can technically rewind to a previous state (which would have different elapsed SOC, elapsed temps, different elapsed time, different capacity, etc) by restoring those variables to that previous state.

Best regards,
Paul.

Thanks a lot for the support, Paul. As I watched one of your videos about battery model updates (believe it was from 2021) I could copy that the LMO/LTO and NMC/Gr models in SAM have advanced equations for capacity fade which includes temperature, but these are not presented in the GUI. I wonder if there is a way to get my hand on those equations. I plan to use your models in a scientific project and need to clarify all details. If I'm not mistaken, there were some references regarding these equations? Best regards Pontus

Hi Pontus,

Published descriptions of these models are listed on the Battery Publications page: sam.nrel.gov/battery-storage/battery-publications.html

In particular:

DiOrio, N.; Dobos, A.; Janzou, S.; Nelson, A.; Lunstrom, B. (2015). Technoeconomic Modeling of Battery Energy Storage in SAM. 32 pp. NREL/TP-6A20-64641 ( PDF 2.6 MB )

Smith, K.; Saxon, A.; Keyser, M.; Lundstrom, B.; Cao, Z.; Roc, A. (2017). Life Prediction Model for Grid-connected Li-ion Battery Energy Storage System. Presented at 2017 American Control Conference. ( PDF 1.4 KB )

Spotnitz, R. (2002). Simulation of Capacity Fade in Lithium-ion Batteries . Journal of Power Sources. Vol 113 pp 72-80 2003.

Best regards,
Paul.