Mathematical Model and Prediction Analysis of Automobile Power Battery
0Mathematical Model and Prediction Analysis of Automobile Power Battery Withdrawal Considering Weibull Distribution
Layman Abstract : The new energy vehicle (NEV) industry has grown rapidly in recent years, but there is a lack of reliable models to predict when and where retired batteries from these vehicles will accumulate. Understanding the future volume of retired NEVs has become an important topic in the industry.
This study develops a prediction model using Weibull distribution, based on national NEV sales data. By calculating model parameters for different vehicle types, the study provides spatial and temporal forecasts of when and where power batteries will be retired.
The model is highly interpretable and shows strong correlations in 90% of the data, making it a useful tool for planning industry strategies and battery recycling efforts. This research offers a solid theoretical foundation for optimizing the management of retired NEV batteries.
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Original Abstract : As a national strategic emerging industry, new energy vehicles have developed vigorously in recent years. However, the industry lacks relevant models for the specific temporal and spatial prediction analysis of retired batteries, and how determining the future amount of retired new energy vehicles has become a hotspot in the industry. In this paper, based on the data of the national new energy vehicle sales terminals, the Weibull distribution is used to construct the retirement volume model, and the model parameters are calculated by vehicle type to realize the spatial and temporal prediction analysis of the retirement of the retired power battery. The model has good interpretation and has a strong correlation in 51% of the model data and correlation in 90% of the model data, which can provide a theoretical basis for the industry layout and research.
View Book: https://doi.org/10.9734/bpi/mcsru/v3/4304
#Weibull_distribuion #new_energy_vehicle #retired_batteries