Polymer lithium battery is widely used in many fields. Its capacity gradually decays with the use time. Accurately constructing capacity decay model and predicting remaining life are crucial to ensure stable operation of equipment and optimize battery management strategy. Capacity decay is affected by the interaction of multiple complex factors, including changes in electrode material structure, electrolyte decomposition, temperature effect, etc., which makes model construction and life prediction extremely challenging.
First, based on electrochemical principles, the electrochemical reaction process inside the battery is analyzed to determine the main decay mechanism, such as loss of active substances and increase in internal resistance. Then, the empirical formula method is used to fit the mathematical relationship between capacity decay and factors such as cycle number, temperature, and charge and discharge rate through a large amount of experimental data. For example, common linear regression models, exponential decay models, etc. At the same time, models based on physical principles can also be used to consider microscopic processes such as lithium ion diffusion and solid phase diffusion inside the battery, and partial differential equations can be established to describe capacity decay. For example, the P2D (Pseudo-two-dimensional) model can simulate the electrochemical behavior inside the battery more accurately, but the computational complexity is high. It is also possible to combine machine learning methods and use the powerful fitting ability of neural networks to train with the working parameters of the battery (such as voltage, current, temperature, etc.) as input and capacity decay as output to obtain a capacity decay model.
One method is to substitute the remaining number of cycles or remaining available capacity into the model based on the established capacity decay model according to the current working state parameters of the battery, thereby predicting the remaining life. Another method is to use data-driven methods, such as support vector machine regression (SVR), to train the historical operating data of the battery, mine the characteristics and laws in the data, predict the future capacity change trend and determine the remaining life. In addition, the Kalman filter algorithm can be combined to estimate and correct the battery state in real time to improve the accuracy and reliability of the remaining life prediction. In practical applications, multiple methods are often integrated to learn from each other to adapt to different application scenarios and battery characteristics.
The research on the construction of polymer lithium battery capacity decay model and the remaining life prediction method will help battery manufacturers optimize product design and production processes and improve battery quality and reliability. For battery users, accurate battery maintenance and replacement plans can be implemented to reduce equipment failure risks and operating costs. In the future, with the continuous development of battery technology and the in-depth application of big data and artificial intelligence technology, it is expected that the accuracy of the model and prediction will be further improved, providing more powerful technical support for the large-scale application of polymer lithium batteries in new energy vehicles, energy storage systems and other fields, and promoting the sustainable development of related industries.
