复杂使用工况下车载锂电池退化建模及剩余寿命预测研究

车载锂电池作为动力源是电动汽车的关键部件，而锂电池的剩余寿命预测是电动汽车健康状态预测与管理的关键问题，对保障电动汽车的安全、稳定、长周期运行具有重要的意义。目前，数据驱动的剩余寿命预测已成为国内外研究的热点。然而，现有数据驱动的锂电池寿命预测方法主要针对环境温度固定、输出电流恒定、不考虑放电深度影响的实验室工况，难以满足实际使用中变温、变流、变放电深度等复杂工况影响的车载锂电池剩余寿命预测的要求。因此，本项目以车载锂电池为研究对象，基于数据驱动的方式，主要研究：1)变温、变流工况下锂电池退化演变基准路径的构建；2)变温、变流工况下锂电池退化建模与剩余寿命预测方法；3)考虑放电深度影响的锂电池剩余寿命预测方法；4)复杂使用工况下导弹发射车车载锂电池退化模型与剩余寿命预测方法的应用验证。本课题的研究成果不仅具有重要的学术意义，而且对于提高电动汽车的可靠性、安全性具有潜在的工程应用价值。

As the power source, vehicle lithium-ion battery is the key component in the electric vehicles, while the remaining useful life prognostic of vehicle lithium-ion battery is the crucial problem in prognostic and health management for electric vehicles, which has important significance to ensure the safety, stability and long period operation of electric vehicle. Recently,remaining useful life prognostic based on data-driven has become one research hotspot. However, most data-driven remaining useful life prognostic methodologies of the lithium-ion battery are committed to fixed ambient temperature, constant discharge current, or without considering the depth of discharge. It is difficult to meet the requirements for remaining useful life prognostic of the lithium-ion battery with time-varying temperature, random variable discharge current and time-varying depth of discharge in the actual operation. In this project, taking vehicle lithium-ion batteries as studying objects, based on data-driven methodologies, we focus on the following : 1) construction of degradation evolution path of vehicle lithium-ion batteries under time-varying ambient temperature and random variation discharge current；2) degradation modeling and remaining useful life prediction methods for vehicle lithium-ion batteries in random variable temperature and discharge current; 3) degradation modeling and remaining useful life prediction methods for vehicle lithium-ion batteries considering depth of discharge; 4) experimental validation and application verification of degradation model and life prediction methods. The research results of this project not only have important academic significance, but also have potential engineering application value for improving the reliability and safety of new energy electric vehicles.