No abstract

The prediction of the health status and remaining useful life of lithium-ion batteries is very important for the safety of electric vehicles and other devices.However, due to the fact that battery residual capacity cannot be measured in real time, the estimation of battery health status is a great challenge for the management system of electric vehicles. At present, machine learning methods have been widely used in battery health state estimation. Based on the experimental data of NASA lithium-ion battery, this article proposes a model based on gradient boosting decision tree (GBDT) model framework and screens effective features from the original battery information indicators to achieve accurate evaluation of lithium-ion battery health state. In this work, many features are extracted from the original charge and discharge data of the battery, and two methods, correlation coefficient and decision tree, are used to screen initial feature, then variance inflation factor (VIF) is used for further screening, finally an efficient iterative method is used to obtain a combination of wellperforming features. The validity of the residual capacity estimation method is proved by the study of NASA battery data set.

Vehicle lightweight and carbon neutrality turn out to be the critical goals in developing new energy vehicles. As an important part of electric vehicles, power battery packs have an impact on the environment. In this study, multiple environmental assessment indicators were grouped into a comprehensive index, namely the green characteristic index, and the green characteristic index was used to comprehensively evaluate the environmental impact of 11 kinds of battery packs. This study calculates

While electric vehicles are widely used, the number of waste lithium-ion batteries is increasing. The recycling and reproduction of materials with high environmental load is the key to the sustainable development of the electric vehicle power battery industry. This study conducted the life cycle assessment of CO 2 , PM 2.5 , SO 2 and NO x emissions in the recycling stage of electric vehicles in the Beijing-Tianjin-Hebei region of China. The relevant conclusions are: electric energy makes a great contribution to pollutant emission. When taking 1 kg as functional unit, the emissions of SO 2 and NO x in the recovery process of lithium iron phosphate (LFP) power battery are lower than those of Lithium nickel manganese cobalt oxide (NMC) battery, while CO 2 and PM 2.5 are opposite. When taking 1 kWh as the functional unit, NMC power battery has better recovery and emission reduction effect than LFP, because it has higher mass and energy density. In particular, the recovery of active materials plays a significant role in NMC battery emission reduction. For CO 2 , recycling does not bring better effects on emission reduction. To achieve carbon neutrality, the recycling process must be optimized. However, for PM 2.5 , SO 2 , and NO x , recycling can in turn help reduce emissions in the production process, and the value is more obvious.