Dear Wenlin Zhang ,

the parameters' errors in LFP battery model(s) are, very much, high(er) near the (hard-won) high SOC (90%, 100%) regime(s), including your case study, with your challenging value:

SOC=92%,

due to high(er) electrochemical (thermal, mechanical stress) inhomogeneity[1-3].

So, let me recommend to, re-make/re-start your troublesome case study using a more favorable ('peaceful') regime of SOC value(s), near 50%, accompanied with, very much, low(er) parameters' errors.

1. Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity in Lithium-Ion Pouch Cells https://www.researchgate.net/publication/336246259_Electrochemical_Thermal-Mechanical_Modelling_of_Stress_Inhomogeneity_in_Lithium-Ion_Pouch_Cells

2. Stochastic many-particle model for LFP-electrodes https://air.unimi.it/retrieve/handle/2434/642548/1219640/CMAT_SDE_revised.pdf

3. Discrete-time modeling of Li-ion batteries with electrochemical overpotentials including diffusion https://www.researchgate.net/publication/351402096_Discrete-time_modeling_of_Li-ion_batteries_with_electrochemical_overpotentials_including_diffusion

Hi Ioannis Samaras, thanks for your response. Could you elaborate on what you mean by using a more favorable regime? SinceI'm doing characterization of the cell, wouldn't it need to cover the entire SOC range?

Hi Wenlin Zhang:

Yes, you need to cover the entire SOC range.

However, the parameters' errors in LFP-battery model(s) are, very much, low(er) around the half (50% +-30%) SOC ('favorable & peaceful') regime.

So, start your next case study using, at least-3-SOC value(s)[1], near 50%.

Then, 'goto 92' (%SOC); it will be more easy for you.

1. A proposal 'educational' list of (initial) SOC value(s): 35%, 50%, 65%.

Hello,

I'd like to share my initial thoughts when reading your question. I've done some charaterization tests on LFP modules before with my previous job..

I was wondering if the operating conditions under which the parameters were initially estimated were the same as that of the drive cycle test? Temperature of the cells has an important influence on the battery parameters (same for the C-rate but to a lesser extent). Another issue could be SOC drift. Due to the flat V-OCV curve of LFP, the estimation of the SOC is more difficult compared to other battery chemistry. So perhaps the SOC estimate you are receiving is not correct. This can potentially be resolved by using the proper CCCV charging current and voltage cutoff provided by the supplier to reset the SOC estimation.

On the model side, do you use the entire relaxation period (the measurement points) in your battery parameter estimation model? If so, perhaps lowering the sample frequency during these moments could help the estimation as to give more weight to the areas where the voltage is changing strongly and/or current is being applied.

Just my thoughts, take them with many grains of salt please!

Cheers,

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Thanks everyone for the input. Just wanted to say that I seemed to have solved this issue by setting proper upper & lower bounds for my parameters. They seemed to have been trapped in local minimum during the LS optimization. I observed the parameter trends and set limits to avoid too much deviation from the previous parameters and that seemed to have solved the problem for now.