A Novel Prediction Process of the Remaining Useful Life of Electric Vehicle Battery Using Real-World Data

Processes

Chieh-Wen Ho¹  Xu Wang²  Jian Li²  Ben-Chang Shia²  Mingchih Chen²  Yi-Wei Kao² 

[1] Artificial Intelligence Development Center, Fu Jen Catholic University, No. 510, Zhongzhen Road, Xinzhuang District, New Taipei City 24205, Taiwan;Graduate School of Business Administration, Fu Jen Catholic University, No. 510, Zhongzhen Road, Xinzhuang District, New Taipei City 24205, Taiwan;

关键词: big data analysis;    remaining useful life;    Lasso regression;    ARIMA;    Monte-Carlo simulation;   

DOI  :  10.3390/pr9122174

来源: DOAJ

【 摘 要 】

In modern society, environmental sustainability is always a top priority, and thus electric vehicles (EVs) equipped with lithium-ion batteries are becoming more and more popular. As a key component of EVs, the remaining useful life of battery directly affects the demand of the EV supply chain. Accurate prediction of the remaining useful life (RUL) benefits not only EV users but also the battery inventory management. There are many existing methods to predict RUL based on state of health (SOH), but few of them are suitable for real-world data. There are several difficulties: (1) battery capacity is not easy to obtain in the real world; (2) most of these methods use the individual data for each battery, and the computing processes are difficult to perform in the cloud; (3) there is a lack of approaches for real-time SOH estimating and RUL predicting. This paper adopts several statistical methods to perform the prediction and compars the results of different models on experimental data (NASA dataset). Then, real-world data were implemented for an online process of RUL prediction. The main finding of this research is that the required CPU time was short enough to meet the daily usage after the real-world data was implemented for an online process of RUL prediction. The feasibility and precision of the prediction model can help to support the frequency control in power systems.

【 授权许可】

Unknown