u003cpu003e Lithium-ion batteries are widely used in consumer electronics, electric vehicles and space systems. However, an unavoidable problem is that the performance of the battery will continue to decline until it is discarded as it is recycled and the material ages. In addition, the degradation of battery performance cannot be directly measured, and it is often necessary to estimate it in advance to decide whether to replace the battery to avoid unnecessary accidents. The current prediction of the cycle life of lithium batteries is still a long way from mature actual online applications. Some foreign universities, research institutes and companies conduct research and development on battery management system (BMS) for electric vehicles, ships, aircrafts and spacecraft. Among them, the prediction of lithium battery pack
cycle life is the core and difficulty of BMS. The portable power supplies used by military electronic equipment such as GPS systems and unmanned aerial vehicles (UAVs) all rely on lithium-ion batteries, and the reliability of lithium-ion batteries must also be evaluated to avoid operational damage due to failure of lithium-ion batteries Serious consequences of varying degrees such as performance degradation or even catastrophic failure. The reliability of rechargeable lithium batteries used in implantable medical devices is recognized by the majority of stakeholders as the most important requirement. To ensure the reliability of lithium batteries in these devices during operation, it is necessary to be able to evaluate lithium The capacity of the battery and the estimated remaining cycle life. There are also some domestic research institutions that have already carried out practical application work, but they are still in the initial stage, such as Liu D． T. Et al. applied the lithium battery pack cycle life prediction method to the satellite lithium battery health assessment system of an aerospace research institute in China, and developed a satellite lithium battery pack remaining life prediction system; for space application computing resource constraints, Zhou Jianbao and others are still in FPGA The RVM-based embedded lithium battery cycle life prediction calculation method is implemented on the platform to predict the remaining useful life (RUL) of the battery; Beijing Jiaotong University and Beijing University of Aeronautics and Astronautics have also studied related battery remaining life estimation methods, and obtained Practical application; Some domestic companies such as Harbin Guantuo Power Equipment Co., Ltd. and Shenzhen Paisidi Technology Co., Ltd. have also achieved certain results in the development of battery management systems. In general, the collection accuracy of battery management systems at home and abroad is still not accurate enough, especially in the accuracy of the remaining cycle life estimation of the battery, and the technology is not mature enough. u003c/pu003eu003c/pu003e
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