Deep learning from three-dimensional Lithium-ion battery multiphysics model Part II: Convolutional neural network and long short-term memory integration

Pang, Yiheng1; Dong, Anqi2; Wang, Yun1; Niu, Zhiqiang3

2024-09-01

10.1016/j.egyai.2024.100398

ENERGY AND AI   影响因子和分区

2666-5468

Lithium-ion (Li-ion) batteries have emerged as a cornerstone of electric vehicles (EVs), enabling the road transportation towards net zero. The success of electric vehicles largely hinges on the battery performance and safety. It is challenging to test and predict battery performance and safety issues by conventional methods, which are usually time-consuming and expensive, involving significant human and measurement errors. To enable the quick estimation of battery performance and safety, we developed three data-driven machine learning (ML) models, namely a convolutional neural network (CNN), a long short-term memory (LSTM), and a CNN-LSTM to predict battery discharge curves and local maximum temperature (hot spot) under various operating conditions. The developed ML models mitigated data scarcity by employing a three-dimensional multi-physics Li-ion battery model to generate enormous and diverse high-quality data. It was found the CNN-LSTM model outperforms the others and achieved high accuracy of 98.68% to learn discharge curves and battery maximum temperature, owing to the integration of spatial and sequential feature extraction. The battery safety can be improved by comparing the predicted maximum battery temperature against safe temperature threshold. The proposed data development and data-driven ML models are of great potential to provide digital tools for engineering highperformance and safe EVs.

Li-ion batteries CNN LSTM Integration Performance Hot spot

ESCI ; EI

英语

其他

Computer Science ; Energy & Fuels

Computer Science, Artificial Intelligence ; Energy & Fuels

WOS:001286533000001

ELSEVIER

Web of Science

1.Univ Calif Irvine, Dept Mech & Aerosp Engn, Renewable Energy Resources Lab RERL, Irvine, CA 92697 USA
2.Southern Univ Sci & Technol, Coll Engn, Sch Syst Design & Intelligent Mfg, Shenzhen, Peoples R China
3.Loughborough Univ, Dept Aeronaut & Automot Engn, Loughborough LE11 3TU, England

Pang, Yiheng,Dong, Anqi,Wang, Yun,et al. Deep learning from three-dimensional Lithium-ion battery multiphysics model Part II: Convolutional neural network and long short-term memory integration[J]. ENERGY AND AI,2024,17.

Pang, Yiheng,Dong, Anqi,Wang, Yun,&Niu, Zhiqiang.(2024).Deep learning from three-dimensional Lithium-ion battery multiphysics model Part II: Convolutional neural network and long short-term memory integration.ENERGY AND AI,17.

Pang, Yiheng,et al."Deep learning from three-dimensional Lithium-ion battery multiphysics model Part II: Convolutional neural network and long short-term memory integration".ENERGY AND AI 17(2024).