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Data-driven predictive prognostic model for power batteries based on machine learning

  • Published:2023-04 Issue: Volume:172 Page:894-907
  • ISSN:0957-5820
  • Container-title:Process Safety and Environmental Protection
  • language:en
  • Short-container-title:Process Safety and Environmental Protection

Author:

Dong Jinxi,Yu Zhaosheng,Zhang Xikui,Luo Jiajun,Zou Qihong,Feng Chao,Ma Xiaoqian

Publisher

Elsevier BV

Subject

Safety, Risk, Reliability and Quality,General Chemical Engineering,Environmental Chemistry,Environmental Engineering

Reference41 articles.

1. Neural network modelling of high pressure CO2 corrosion in pipeline steels;Abbas;Process Saf. Environ. Prot.,2018

2. AdemFr, Rubbedikatz Hannes Knobloch, moxkari (2019) dsr-18/long-live-the-battery[Source code]. https://github.com/dsr-18/long-live-the-battery.

3. Closed-loop optimization of fast-charging protocols for batteries with machine learning;Attia;Nature,2020

4. Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach;Bagherzadeh;Process Saf. Environ. Prot.,2021

5. Critical thickness of nano-enhanced RT-42 paraffin based battery thermal management system for electric vehicles: a numerical study;Bais;J. Energy Storage,2022
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