capacity estimation coupled with SOH estimation, Li-Ion cell

Goodmorning,

I'm trying to evaluate the state of health of a Li-Ion cell comparing the estimated results with the experimental data. In order to do this, I'm using the Capacity estimation block coupled with the SOH block (capacity based). I used the SOC estimation block as input for the capacity estimator and the parameters of the battery were set using Battery (table-based) block, in which I also set the capacity fade using the equation option already implemented in the block.

I'm not able to find reliable results because the capacity increases during the charging phase and decreases during the discharge, in such a way that the SOH oscillates from 1 to 0.6 or even to 0 after few cycles. Instead, using the SOH estimation based on the resistance increase I can obtain reliable results. I would like to compare the results using both methods.

I kindly ask what can be wrong in the estimation of the capacity or if the problem is in something else.

Thank You in advance,

Irene

[akshatsood]

Hi @irene giusti, could you please share the model and any additional details so that I can reproduce the issue on my end and offer insights to address the issue.

[irene giusti]

Hi @akshatsood, you can find attached the files. I have added a 'Mean' block in order to remove the oscillations which come from the 'Capacity estimation' block, but I'm not sure it's the correct choice. Thank You.

[akshatsood]

Hi @irene giusti, thank you for sharing the files. To be on the same ground, I would like to confirm that SOH estimated from the below section of model is far from expected.

While, for the below segment of mode, estimated SOH is in line with the expected value.

Please correct me if I am wrong in understanding the question.

[irene giusti]

the SOH estimation based on the resistance increase works if you set in the battery characteristics the fade only in the resistance. The problem is that it's almost impossible to find experimental results about the internal resistance increase during life cycle; instead I have results regarding capacity, from which I created a lookup table (fade_percentage_cap).

The SOH (capacity based) oscillates from 1 to 0 because the capacity estimation oscillates between 1.5 and 8. Trying to solve the problem, I added the 'mean' block so now my question is: has it a physical meaning or not? Is it normal that the estimated capacity oscillates in such a way? or is there a problem in the set-up?

[akshatsood]

The approach to estimate the State of Health (SOH) based on resistance increase is indeed viable when battery degradation primarily manifests as an increase in internal resistance. However, the challenge of scarce experimental data on resistance changes over the lifecycle makes this method less practical. Your strategy to construct a lookup table using capacity fade data is an alternative, as capacity metrics are more readily available.

The oscillation of the SOH from 1 to 0 due to the capacity estimation fluctuating between 1.5 and 8 suggests that there may be an issue with the setup or the model itself. Normally, you would expect the capacity to decrease gradually, not oscillate, as the battery degrades. The inclusion of a 'mean' block seems to be an attempt to smooth out these fluctuations, but it's crucial to consider whether this averaging has a physical basis or if it merely masks the underlying problem.

It is not typical for the estimated capacity to oscillate so widely, and it could indicate that the model or the input data is not accurately capturing the battery's behavior. This could be due to incorrect assumptions in the model, or other factors. This requires a more detailed investigation and I would share any updates I will have during the investigation.

[irene giusti]

I really appreciate, thank you

[akshatsood]

Did you get a chance to go through this MATLAB Answer, it seems to address a similar issues pertaining to SOH. Kindly go through it and let me know if it helps in address the issue.

https://in.mathworks.com/matlabcentral/answers/2051562-why-soh-decreases-in-few-cycles?s

[irene giusti]

Yes, thanks to that answer I resolved the problem I had related to SOH based on resistance increase, now the model works setting only the conditions of resistance fade. Going on I realized that it was not easy to find data about the internal resistance in order to validate the model, so it will be easier to analyse the lifecycle referring to capacity degradation.

[akshatsood]

Hi @irene giusti, glad to know that. I am moving this discussion to the "Answers" section so that it helps someone out facing a similar issue as yours.

Answer 1

[akshatsood]

Hi @irene giusti,

I understand that you are observing a difference in the estimated data and experimental data. On investigating the files shared and understanding the requirements highlighted in the question, I put forward the following observations

1. The approach to estimate the State of Health (SOH) based on resistance increase is indeed viable when battery degradation primarily manifests as an increase in internal resistance. However, challenge of scarce experimental data on resistance changes over the lifecycle makes this method less practical. Your strategy to construct a lookup table using fade data is an alternative, as capacity metrics are more readily available.
2. The oscillation of the SOH from 1 to 0 due to the capacity estimation fluctuating between 1.5 and 8 suggests that there may be an issue with the setup or the model itself. Normally, you would expect capacity to decrease gradually, not oscillate, as the battery degrades. The inclusion of a 'mean' block seems to be an attempt to smooth out these fluctuations, but it is crucial to consider whether this averaging has a physical ground or if it merely masks the symptoms of an underlying problem that needs to be addressed.
3. It is not typical for the estimated capacity to oscillate so widely, and it could indicate that the model or the input data is not accurately capturing the battery behavior. This could be due to incorrect assumptions in the model. This requires a more detailed investigation and I would share any updates I will have.

Further, I would request you to go through the following MATLAB Answer, it seems to address a similar problem pertaining to SOH. Do let me know if it helps in resolving the problem.

https://www.mathworks.com/matlabcentral/answers/2051562-why-soh-decreases-in-few-cycles

I hope this helps.

[irene giusti]

Thank You for the answer, I will investigate in order to find the error in the battery model settings