With the retreat of the new energy subsidy policy and the number of electric vehicle fire and combustion safety accidents last year, the original hot ternary lithium battery gradually cooled down, and the lithium iron phosphate battery with good safety, long life and excellent safety gradually gained people's attention. The service life of electric vehicles can usually reach 6-10 years, so relatively high requirements are put forward for the service life of power lithium batteries, so the prediction and analysis of the cycle life of power lithium batteries is particularly important.

Recently, Maik Naumann (first author, corresponding author) of Technical University of Munich, Germany, and others analyzed the cycle life decline of commercial lithium iron phosphate batteries from Sony Corporation, and established a model, which predicts the error of capacity loss Less than 1%, and the prediction error about internal resistance is less than 2%.

In the experiment, the author used Sony’s US26650FTC126650 lithium battery as the research object. The positive electrode material of the battery is LFP, and the negative electrode is graphite. The constant current and constant voltage discharge mode is enabled, that is, discharge with a constant current of 1C to 2.0V, and then discharge with a constant voltage until the current drops to 150mA). The basic information of the battery is shown in the table below.

In the experiment, the author arranged a total of 19 experimental points (as shown in the table below), and tested the effects of different temperatures, different discharge depths, different SoC ranges, and different charge and discharge currents on the battery cycle life.Also read:custom lithium battery manufacturers

- Influence of charge and discharge rate

The figure below shows the change of capacity and internal resistance of the LFP battery during the cycle (discharge depth 80%, 10%-90% SoC, ambient temperature 40°C) under different charge and discharge rates. With the increase of time and the number of equivalent cycles, all LFP batteries with charge and discharge currents have a decrease in capacity and increase in internal resistance, in which the decline rate of battery capacity gradually decreases, showing a linear relationship with the square root of time, which is consistent with Some previous studies are consistent, mainly due to the influence of the growth law of the SEI film on the surface of the negative electrode, while the increase rate of the internal resistance of the battery remains constant.

From Figures a and b below, it can be seen that the battery with a larger charge and discharge current has more serious capacity decline and internal resistance increase during the cycle. Usually we think that the SEI film is the cause of lithium battery cycle and storage. Therefore, the decline of the lithium battery should be closely related to the total capacity of the battery charge and discharge, so the author analyzed the relationship between the capacity decline and the increase in internal resistance and the equivalent cycle number (as shown in Figure c and d), from the following figures c and d, it can be seen that the higher charge-discharge rate in the equivalent cycle diagram causes less lithium battery capacity decline and internal resistance increase, which is mainly because of the high rate It takes less time to cycle the same number of times, which makes the battery’s calendar life decline less. Therefore, if we deduct the calendar life decline, we can find that the battery’s capacity decline and internal resistance increase still need to be increased under high rates. Faster than at small magnifications.

- Influence of discharge depth

The figure below shows the capacity and internal resistance changes of lithium iron phosphate batteries at different discharge depths. The average SoC of all batteries is 50%, the charge and discharge are performed at 1C, and the ambient temperature is 40°C. From Figure a below, it can be seen that the decline trend of lithium iron phosphate batteries is closely related to the depth of discharge. Only 100% DOD and 80% DOD batteries have a linear relationship with the square root of the cycle time/equivalent cycle number. The batteries with lower DOD cycle show a linear decline trend in the early stage (as shown in Figure a below), the fastest decline is the battery with 10% and 20% DOD cycle, while the battery with 5% and 40% DOD declines The falling speed is slower. After reaching 1000 equivalent cycles, all batteries with a depth of discharge less than 80% DOD stopped decaying, and the two batteries with 10% and 20% DOD decayed faster at the initial stage, after 1000 equivalent cycles The capacity of the battery begins to rise slowly, and after completing all 10600 equivalent cycles, the capacity retention rate of the battery with a shallower discharge depth will be relatively higher.

- The influence of temperature

Temperature has a decisive influence on the speed of electrochemical reaction, so temperature will also have a significant impact on the decay speed of lithium batteries. Therefore, the author tested lithium iron phosphate batteries at 25 ° C and 40 ° C, and 100% DOD and 80 Cycle performance at %DOD. As can be seen from Figure a below, there is almost no difference between 25°C and 40°C in the first 8,000 cycles of a battery discharged at 80% DOD. For a battery with 100% DOD, there is almost no difference between 25°C and 40°C in the first 4,000 equivalent cycles. It is almost the same, and the difference begins to appear after 4000 equivalent cycles, but overall, the two common temperatures of 25°C and 40°C in daily use have little effect on the decline of lithium iron phosphate batteries.

model building

According to the above experimental data, the author established a life decay model. Since the experimental data show that the temperature has little effect on the cycle performance of the battery, the author did not consider the effect of temperature in the model.

- Model structure

The capacity decline during the lithium battery cycle comes from two parts: cycle decay and calendar decay. Therefore, in the model, the author also divides the model into a cycle decay model and a calendar storage decay model. The cycle decay model also includes The basic information of the model is shown in the table below.

- Influence of charge and discharge capacity

From the above data, it can be seen that the capacity decline of lithium batteries has a linear relationship with the square root of the battery’s charge and discharge capacity, so the parameter ZcycQloss=0.5 in the capacity decline model, and the increase rate of internal resistance is linear with the charge and discharge capacity Relationship, so the parameter ZcycRinc=1 in the internal resistance model.

- Effect of magnification

In order to be able to separate the influence of the rate on the capacity decay and internal resistance increase of the iron phosphate battery, the author sets the factor KDOCQloss, which affects the capacity decay of the depth of discharge, to 1, and by fitting the above formula 5, the rate can be obtained The influencing factor KC-rateQloss/Rinc, the fitting results are shown in Figures a and c below. From Figure a below, it can be seen that there is a linear relationship between KC-rateQloss and the magnification, so we can convert KC-rateQloss into the following formula 8 The form shown, and calculate the value of a and b in it. According to the value of KC-rateQloss, the author fits the capacity decline (the fitting result is shown in Figure b below). From the figure, it can be seen that the fitting result and the test result are combined very well. Under 0.2C and 0.5C magnification The error between the fitting results and the experimental results is less than 0.5%, and the fitting error at the end of life under 1C magnification is also less than 1.5%.

From the figure c below, it can be seen that the linearity between the increase in internal resistance of the battery and the rate is not good, so the fitting results and the experimental results are not very good, and only the battery fitting results and the experimental results of the 1C rate cycle The error of the results is within 2%, and the batteries cycled at 0.2C and 0.5C rate are only better in the first 3000 cycles.

- Influence of discharge depth

The depth of discharge has a significant impact on the cycle performance of lithium batteries. From the previous data, when the depth of discharge is lower than 80%, the battery will appear in many different forms of decline, so the author here is only 80% and 100%. DOD depth of discharge batteries were simulated. Among them, KDOCQloss can be obtained by fitting the following formula 9, and then converted into a relationship with the depth of discharge according to the following formula 10.

According to the obtained parameter results, the author fits the cycle data. From the figure b below, it can be seen that the fitting results are slightly worse at the beginning, but the fitting results at the end of the life are in good agreement with the experimental results. This is important. Because the authors used end-of-life data to fit the results.Also read:500ah lithium battery

composite model

The decline of lithium iron phosphate battery during the cycle comes from two factors: cycle decline and calendar storage decline, so the author integrates the two influencing factors into one model to fit the decline of lithium iron phosphate battery .

Since temperature has a crucial influence on the calendar storage life decay of lithium batteries, the authors performed a fitting with storage data obtained at 40 °C. From the fitting results, the error is less than 0.5%. The model shows that the capacity decay due to calendar storage is about 9.21%, which is very close to the experimental data of 9.24%, while the capacity loss due to cycling About 3.64%, accounting for about 28.35% of the entire battery capacity loss. In the simulation of internal resistance, the model simulated the increasing trend of internal resistance well in the first 500 times, but then the error began to increase, and the final error increased to about 2%.