Importance of estimating the remaining life of lithium batteries

Lithium-ion batteries are widely used in consumer electronics, electric vehicles, and space systems. However, an inevitable problem is that the battery performance will continue to decline until it is abandoned as it is recycled and the material is aging. In addition, the degradation of battery performance can not be directly measured, and it is often necessary to estimate it in advance to decide whether to replace the battery to avoid some unnecessary accidents.

At present, the prediction of the cycle life of lithium batteries is still some distance from the actual online application. Some foreign universities, research institutes and companies have conducted research and development on battery management systems for electric vehicles, ships, aircraft and spacecraft(BMS), and the prediction of lithium battery cycle life is the core and difficulty of BMS. Many military electronic devices such as GPS systems and drones(UAVs) require portable power supplies that rely on lithium ion batteries. They also need to evaluate the reliability of lithium batteries. In order to avoid the failure of lithium-ion batteries, there are serious consequences ranging from operation damage to performance degradation or even catastrophic failure. The reliability of rechargeable lithium batteries used in implanted medical equipment is recognized by a wide range of stakeholders as one of the most important requirements to ensure the reliability of lithium batteries in these devices during operation. The ability to assess the capacity of lithium batteries and predict the remaining useful life cycle must be required.

There are also some research institutions in China that have already carried out practical application work, but they are still in the initial stage. For example, LiuD. T. et al. applied the method of predicting the cycle life of lithium-ion batteries to aspecial lithium-ion battery health assessment system in China. The residual life prediction system ofspecial lithium-ion battery was developed. In order to solve the problem of space application computing resource constraints, Zhoujianbao et al. also implemented the RVM-based embedded lithium battery cycle life prediction calculation method on the FPGA platform to predict the remaining life of the battery(remaining useful life, RUL); Beijing Jiaotong University and Beijing University of Aeronautics and Astronautics have also studied the method of estimating the remaining life of related batteries, and have been applied in practice. Some domestic companies such as Harbin Guantu Power Equipment Co. Ltd. and Shenzhen Shiji Technology Co. Ltd. have also achieved certain results in the development of battery management systems.

Generally speaking, the accuracy of the battery management system is still not accurate, especially the accuracy of the estimate of the battery's remaining cycle life is still insufficient and the technology is not mature enough.

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