State of Health Prediciton Model for LFP/Graphite Type Lithium-Ion Batteries Based on Discharge Curve Analysis
Authors: Nishijima Shun , Honkura Kohei 
Published: December 2024

Introduction

To improve reliability and maximize lifespan of products loading lithium-ion batteries (LIBs), development of internal state diagnosis and life prediction technology for LIBs are desired. In our previous research, we reported discharge curve analysis (DCA) as diagnosis method for internal state of LIBs and life prediction technique based on DCA results 1), 2). In DCA, by separating discharge capacity (Q) – open circuit voltage (OCV) curve and Q – resistance (R) curve of a battery into those of positive and negative electrode, we can quantitatively evaluate how positive and negative electrodes and electrolyte are degraded. Since development of DCA has focused on NMC/graphite type LIBs whose voltage change continuously over SOC, it is necessary to extend the DCA methodology to two-phase coexisting materials which have plateau potential, such as LFP/graphite type LIBs that have been attracting attention recently.

Based on the above background, we developed extended DCA, which can be applicable to two-phase coexisting materials by combining Q-OCV curve and Q-R curve analysis3). In this paper, we report the results of constructing state of health (SOH) prediction model based on the extended DCA results.

Experimental

Commercially available LFP/graphite type cylindrical cells were tested in various calendric and cycling aging conditions. Then, galvanostatic intermittent titration technique (GITT) measurement was performed regularly which repeat pulse discharge and rest from SOC=100% to 0%. By analyzing the GITT measurement data, Q-OCV curve V(Q) and Q-R curve R(Q) of cells were obtained in each test period. LFP and graphite half cells with Li metal as counter electrode were manufactured and tested similar GITT measurement. Then Q-OCV curves (Vp
(qp
) and Vn
(qn
)) and Q-R curves (rp
(qp
) and rn
(qn
)) of each LFP and graphite were obtained. In the extended DCA, Q-OCV curve of a cell was calculated from Eq. (1) and (2) and Q-R curve of a cell was calculated form Eq. (1) and (3). Then each calculated curve was fitted to the actual measured curve with mp
, mn
, δp
, δn
, ap

,
an
and R0
as variable parameters and the values of those parameters were determined.

Q = mpqp
- δp
= mnqn
- δn

-Eq. (1)

V(Q) = Vp
(qp
) - Vn
(qn
)
-Eq. (2)

R(Q) = ap
/mp
*rp
(qp
) + an
/mn
*rn
(qn
) + R0

-Eq. (3)

mp, mn

Amount of active materials [g]
δp, δn

Electrode capacity outside of cell voltage [Ah]

qp, qn

Discharge capacity of electrodes [Ah/g]
ap, an

Coefficent of rp and rn [-]

Vp, Vn

OCV of electrodes [V]
R0

Ohmic resistance [Ω]

rp, rn

Resistance of electrodes [Ωg]
-
-

*p or n as a subscription represent positive or negative electrode.

SOH prediction model was constructed by modeling the time series data of above seven parameters determined by extended DCA. Each parameter was fitted with a time function using a power law and the coefficients within the function were expressed as polynomials with the test conditions (temperature, SOC and C-rate). Using the predicted values of each parameter obtained from time functions, SOHQ and SOHR were predicted.

Results and Discussions

The SOHQ and SOHR prediction accuracy of LFP/graphite type cells is shown in Fig. 1. The time dependence behavior of SOHQ and SOHR prediction error in calendric aging is shown in (a) and (b) and in cycling aging is shown in (c) and (d). As shown in (a) and (b), both SOHQ and SOHR can be predicted with an error less than 5pt in calendric aging by constructing the model using the first half of test period as training data. As shown in (c) and (d), developed model can predict SOHQ with a prediction error less than 5pt and SOHR with a prediction error under 7pt in cycling aging.

Conclusion

In this report, we developed SOH prediction model for LFP/graphite type cells by converting quantitative parameters obtained by extended DCA into time functions. As a result, the developed SOH prediction model could predict SOHQ and SOHR with an error less than 5pt in calendric aging, and less than 5pt for SOHQ and 7pt for SOHR in cycling aging.

In the poster session, we will also report about the details of extended DCA method, aging trends of cells and the evaluation results of the time-split cross-validation of the developed SOH prediction model.

References

1)K. Honkura et al., J. Power Sources, 196, 10141-10147(2011)

2)K. Honkura et al., The Electrochemical Society of Japan, 89(2), 133-139 (2021)

3)S. Nishijima et al., “Degradation state diagnosis of lithium-ion batteirs based on internal resistance properties”, The 63rd Battery Symposium in Japan, Fukuoka, Japan, Nov 2022

Figure 1