Research On Lithium Ion Battery Cycle Life

1. Factors affecting the lithium ion battery cycle life
   1. Aging and decay of battery materials
   2. Battery charging and discharging system
   3. Temperatures that affecting battery lifespan
   4. Monomer consistency that affects battery lifespan
2. Cycle life prediction of battery
   1. Battery lifespan prediction based on capacity decay mechanism
   2. Battery lifespan prediction based on feature parameters
   3. Data-driven forecasting
3. Conclusion

This is a review of the latest research on lithium ion battery cycle life. The influencing factors of cycle life, including aging and decay of battery materials, charge-discharge regime, temperature, and cell consistency, etc., are emphatically expounded, and the prediction method of lithium battery cycle life is introduced. For energy-type batteries, it is generally considered that the service life of the battery is terminated when the usable capacity of the battery decay to 80% of the initial capacity. The life of a battery includes cycle life and calendar life. The calendar life refers to the period of time from the date of production to the end of life of the battery, measured in years. The cycle life of refers to the number of charge and discharge cycles that the battery can withstand before the battery capacity decays to a specified value under a certain charge and discharge system. Many complex physical and chemical reactions occur during the charging and discharging process of lithium batteries, so there are many factors that affect the lithium ion battery cycle life. Correct assessment of battery life has a certain guiding role in the production and development of lithium batteries and battery health management systems.

1. Factors affecting the lithium ion battery cycle life

There are some factors that can affect the lithium ion battery cycle life.

① Aging and decay of battery materials

Aging and decay can also be the reasons of lithium ion battery cycle life. The materials inside the lithium battery mainly include: positive and negative active materials, binders, conductive agents, current collectors, separators and electrolytes. During the use of lithium batteries, these materials will be accompanied by a certain degree of decline and aging. physicist believe that the capacity attenuation factors of lithium manganate batteries include: dissolution of positive electrode material, phase change of electrode material, decomposition of electrolyte, formation of interface film and current collector corrosion. Experts conducted a systematic and in-depth analysis of the change mechanism of the positive electrode, negative electrode and electrolyte of the battery during cycling. The authors believe that the formation and subsequent growth of the negative SEI film will be accompanied by irreversible loss of active lithium, And the SEI film does not function as a true solid electrolyte, except for lithium ions, the diffusion and migration of other substances can lead to the gas generation and particle rupture. In addition, the change in material volume and the precipitation of metallic lithium during cycling can also lead to capacity loss. Experts also disassembled the positive and negative electrode plates of lithium cobalt oxide batteries after cycling at 25 and 40°C. The results of SEM, XRD and FTIR tests showed that both positive and negative active materials were lost. They analyzed the electrical properties of lithium iron phosphate power batteries cycled 6000 times. The capacity retention rate was 84.87%, the AC internal resistance increased by 18.25%, and the DC internal resistance increased by 66%. The author disassembled the cycled battery, and performed the button battery performance test and SEM analysis respectively. It was found that the performance of the negative electrode material decayed rapidly after the cycle, and the expansion of the negative electrode volume and the thickening of the SEI film was the main influencing factors that affect lithium ion battery cycle life

② Battery charging and discharging system

The charging and discharging system mainly includes three aspects: charging and discharging method, rate and cut-off condition. In terms of charging method, American scientist Maas once put forward the concept of the best charging curve. These factors can also affect lithium ion battery cycle life. He believes that the best charging current of the battery gradually decreases with the prolongation of charging time: I=I0e-αt. In the formula: I is the acceptable charging current; I0 is the maximum initial current at the moment of t=0; t is the charging time; α is the decay constant. In the figure, the lower part of the curve is the rechargeable area. Charging in this area will not cause damage to the battery. If the charging current exceeds this area, the polarization will increase, which will not only fail to improve the charging efficiency, but also lead to serious gas evolution and shorten the lithium ion battery cycle life. Scientists made an all-round comparison of various methods of charging, discovering that constant current charging in the later stage due to excessive current causes gas evolution inside the battery and damages the battery; while constant voltage charging in the early stage of charging, the current is too large, which directly damages the battery; reverse pulse charging can effectively eliminate polarization, but it has a certain impact on lithium ion battery cycle life. The charge-discharge rate and cut-off conditions also have a great influence on the battery cycle life. Experts studied the cycle performance of 18650 lithium cobalt oxide batteries at different discharge rates, and found that the capacity loss rates after 300 cycles of 0.5C, 1C and 2C discharge rates were 10.5%, 14.2% and 18.8%, respectively. And through the analysis, it is concluded that the change of the positive electrode material structure and the thickening of the negative electrode surface film will lead to the reduction of the number of lithium ions and the blockage of the diffusion channel, thus causing the battery capacity to decay.Experts increased the charge cut-off voltage of lithium cobalt oxide battery from 4.2V to 4.9V, and found the electrode material’s structure hanged by testing the entropy curve of different SOC of the electrode after charging.

③ Temperatures that affecting battery lifespan

Different types of lithium ion batteries have different optimal using temperatures. Researchers reported the effect of temperature on the cycle performance of Sony 18650 lithium cobalt oxide batteries. The study found that when the test temperature exceeds 50 °C, the battery decay significantly faster than the normal temperature and 45 °C (Figure 3), and the capacity at high temperature decays. Attributable to the decomposition and regeneration of the SEI film of the battery anode, the loss of active lithium and the increase of the anode impedance. Experts compared the electrical properties of the 18650 lithium iron phosphate/graphite power battery at different temperatures, and obtained similar results: the capacity decay of the battery is relatively slow when cycled at room temperature, while at 55 and 65 °C high temperature conditions, the battery exhibits rapid failure behavior. The authors believe that the trace amount of iron deposited on the graphite anode will catalyze the formation of its interface film, which has a certain effect on the capacity fading. Experts studied the performance of lithium batteries at low temperatures, and found that when the temperature is lower than -10°C, the capacity of the battery decayed rapidly, and analyzed the reasons for the poor low temperature performance, in addition to the decrease of the ionic conductivity of the electrolyte. It is also related to the electrode material. The authors compared the EIS curves of the full cell and the positive and negative symmetrical electrodes with temperature, and found that when the temperature is lower than -10°C, the impedance of the full cell and the half cell has an upward trend, especially the charge transfer impedance will rise sharply, and dominate.

④ Monomer consistency that affects battery lifespan

A battery pack generally connects hundreds or thousands of single cells in series and parallel. In addition to the above-mentioned influencing factors, the cycle life of the battery pack is another important factor. Due to differences in materials and manufacturing processes, it is difficult to guarantee the consistency of lithium batteries. In terms of materials, the uniformity of positive and negative electrode materials and electrolytes is very important, and the consistency of lithium batteries produced in the same material and batch is often relatively good. In terms of manufacturing, the production process of lithium batteries is very complicated, and each step will involve multiple process parameters. If the control is not good, it will lead to inconsistencies in parameters such as battery voltage, capacity, and internal resistance. Experts studied the effect of single cell inconsistency on the service life of the battery pack. They believed that the life of the battery pack was always shorter than the life of the single cell with the shortest lifespan times, and the increase in battery life is disproportionate to the increase in battery life. Based on the Thevenin equivalent circuit, physicists investigated the influence of the ohmic resistance, capacity and polarization difference of the single cell on the performance of the series battery pack, and found that the capacity difference had the greatest impact. Before the battery is actually applied in a group, it will go through the screening and grouping process, and eliminate the monomers with large differences in performance parameters, so as to minimize the impact of the differences in the battery manufacturing process on the performance. However, the rapid detection of battery self-discharge is a research difficulty. The self-discharge of the single battery will cause the SOC of each battery in the battery pack to be inconsistent, which will affect the capacity of the entire battery pack. Generally speaking, the higher the temperature, the greater the self-discharge of the battery. If the design of the battery pack box is unreasonable, the internal resistance and self-discharge degree of the batteries in different positions will be affected to a certain extent due to the difference in heat dissipation.

2. Cycle life prediction of battery

Due to the long time and high cost of battery cycle life testing, the establishment of life models and life evaluation and prediction have become research hotspots for domestic and foreign scholars. The life prediction methods of lithium batteries can be divided into three categories according to information sources: prediction based on capacity decay mechanism, prediction based on characteristic parameters and prediction based on data driven.

① Battery lifespan prediction based on capacity decay mechanism

Mechanism-based prediction is based on the aging and decay mechanism of the internal structure and materials of the battery during cycling to infer the battery life. This method requires the use of basic models to describe the physical and chemical reaction processes that take place inside the battery, such as Ohm’s law, electrochemical polarization, concentration polarization, and internal diffusion of electrode materials. Based on the loss of active lithium during cycling, experts used first-principles to simulate the capacity decay model of lithium cobalt oxide batteries. The influencing parameters include exchange current density, DOD, interfacial membrane impedance, and charge cut-off voltage. The author compares the obtained life prediction model with the measured data, and finds that the model is very close to the actual test results. Virkar proposed a battery degradation model based on non-equilibrium thermodynamic, taking into account the effects of chemical potential and SEI film on capacity degradation, and pointed out that there will be unbalanced cells in the series battery pack, and the interface between the positive electrode and the electrolyte is SEI films may also be generated, leading to increased capacity fading.

② Battery lifespan prediction based on feature parameters

Prediction based on characteristic parameters refers to predicting the battery life by using the changes of some characteristic factors during the aging process of the battery. At present, researchers pay the most attention to the relationship between EIS and cycle life. Experts studied the change of impedance spectrum of commercial lithium cobalt oxide battery during 1C charge-discharge cycle, and observed the change of electrode material by XRD, TEM and SEM, and found that in the Nyquist curves of the positive and negative electrodes of lithium batteries, corresponding to the size of the semicircle in the low-frequency region of the interface membrane impedance increases with the increase of the number of cycles, and lithium ion battery cycle life can be inferred accordingly. EIS can give a relatively detailed description of battery impedance, but the test instrument is susceptible to external interference and it is difficult to analyze complex spectra effectively. Relatively speaking, the measurement of pulse impedance is simple and easy, and online monitoring can be realized quickly.

③ Data-driven forecasting

The data-driven method refers to directly analyzing the test data to mine the rules without considering the physical and chemical reactions and mechanisms inside the battery, which is an experience-based simulation method. The more common ones are time series model (AR), artificial neural network model (ANN) and correlation vector method (RVM). The AR model infers the predicted value in the current state based on the data measured at some previous time points, and has a linear characteristic. Considering the nonlinear relationship between battery capacity decay and cycle times, Luo Yue proposed an improved nonlinear AR model, which introduced an accelerated degradation factor in the later stage of prediction to improve the accuracy of prediction. ANN model is an artificial intelligence network system composed of multiple neurons according to certain rules, which is a typical nonlinear model. The RVM model belongs to the data regression analysis method, which can flexibly control over-fitting and under-fitting by adjusting parameters, and has the characteristics of probabilistic prediction. The prediction method based on the internal mechanism has better theoretical support and better accuracy, but the complexity is large. The advantage of the data-driven method is that it is simple and practical, but because the acquired data cannot cover all parameters, it also has certain limitations.

3. Conclusion

This paper mainly introduces the factors affecting the lithium ion battery cycle life and the research on the life prediction model. It can be seen that there are many factors affecting the lithium battery cycle life, and for lithium batteries of different materials and structures, the influencing factors are also different. From the analysis in this paper, we can know that we can prolong the battery life by controlling the parameters, such as making the battery work under the appropriate temperature, rate and charging and discharging conditions. Relatively speaking, the factors affecting the cycle life of the battery pack are more complex, because these factors will have a mutual coupling effect, and the problem of cell consistency will lead to the insufficient performance of the battery pack, which will seriously shorten the cycle life of the battery pack life. When predicting the lithium ion battery cycle life, it is possible to establish an accurate, reasonable and simple and operable model based on the internal mechanism of the battery, a certain characteristic parameter or a large amount of measured data to accurately evaluate the lithium ion battery cycle life and further optimize its performance of great significance.