What is the main reason for the lithium ion battery capacity attenuation?

Source: 2020 - 04 - 04 13:09 hits: times of lithium ion battery life prediction based on particle filter abstract: in view of the traditional methods couldn't solve the lithium ion battery residual life prediction of nonlinear system, this paper proposes a battery life prediction method based on particle filter algorithm. It can effectively solve the problem of nonlinear, accurately realize the battery life prediction. Key words: life prediction; Particle filter; Degradation ability; Nonlinear of lithium ion battery discharge current, temperature, battery materials, and many other factors, affect the residual capacity of battery discharge voltage, current, temperature and battery charging and discharging the history of the parameters, such as complex function, the service life of lithium ion battery is a nonlinear prediction problem, the problem such as the traditional prediction methods is insufficient to meet demand, this paper proposes a battery life prediction method based on particle filter. 1 lithium ion battery life model is based on the capacity of lithium ion battery capacity decay is the main reason of the negative reaction of the battery. Brous et al. Analysis of the lithium battery in different voltage and temperature of battery capacity attenuation situation, as shown in the following type: type of x as the relative capacity of cell loss ratio; For constant K, n, d, S, e0, n represent ion area, thickness and conductivity of the membranes. This model only considers the temperature ( 15 ~ 60℃) Impact on the service life of battery capacity, does not involve ion battery voltage, has great limitations. Not long ago, Liaw et al. [ 2] Put forward from the Angle of the battery capacity attenuation, the use of ECM equivalent circuit model to predict the storage life of the battery. The model not only fitting degree is high, and can simulate the battery aging and after discharge behavior under different rate. It can be expressed as: ( 2) Type: where V0 is refers to the open circuit voltage, discharge Q ( 0) Is the initial capacity of the battery, from the formula, the battery voltage is the discharge current and ohmic contact R1, R2 battery electrochemistry reaction function, for constant cell constant R1, R2 nonlinear changes of aging time, the fitting formula is as follows: R2 = (a + b SOC) C + dexp [ ( 1 - soc) C] ( 3) Type: a, b, c, d, e are lithium ion batteries charged status and function of the aging time t. For most of the lithium ion battery, its capacity over time has a clear rule, use this rule, you can better predict the battery life. 2 battery capacity degradation experiment under laboratory conditions, FIG. 1) With the increase of cycle count, particles gradually reduce energy stored in the battery. Abscissa to charge and discharge cycles, k, c, y coordinate for the volume data here for naturalization. When the capacity of 0. 7, battery reach the point of failure. As can be seen from the figure 1, before k = 160, particle filter algorithm mainly depends on the observation data of capacity. After filtering to get the value of a, b, c, d, predict the future trend of 1200 k. Graph, the curve A is measured as A result, the curve B for the results, the untreated curves for the prediction of particle filter for curve C. It can be seen that the predictions of a particle filter and the actual value are basically identical. Three conclusions based on the nonlinear lithium ion battery system residual life prediction problem, put forward a kind of battery life prediction method based on particle filter algorithm. The research is the complex noise of non-gaussian nonlinear system under the condition of remaining life prediction provides a practical solution, the study of nonlinear battery life prediction has important reference value.