Python‐based scikit‐learn machine learning models for thermal and electrical performance prediction of high‐capacity lithium‐ion battery. (23rd August 2021)

• Record Type: Journal Article
• Authors: Tran, Manh‐Kien; Panchal, Satyam; Chauhan, Vedang; Brahmbhatt, Niku; Mevawalla, Anosh; Fraser, Roydon; Fowler, Michael
• Is Part Of: International journal of energy research. Volume 46:Number 2(2022)
• Page Start: 786
• Page End: 794
• Publication Date: 2021-08-23
• Subjects: battery modeling -- lithium‐ion battery -- machine learning -- multivariate multioutput regression -- scikit‐learn -- thermal modeling; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.7202
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: With the increasing popularity of electric vehicles (EVs), the demands for rechargeable and high‐performance batteries like lithium‐ion (Li‐ion) batteries have soared. Li‐ion battery systems require the use of a battery management system (BMS) to perform safely and efficiently. Accurate and reliable battery modeling is important for the BMS to function properly. Currently, many BMS applications use the equivalent circuit model due to its simplicity. However, with the development of a cloud BMS, machine learning battery models can be utilized, which can potentially improve the accuracy and reliability of the BMS. This work investigates the performance of four different machine learning models used to predict the thermal (temperature) and electrical (voltage) behaviors of Li‐ion battery cells. A prismatic Li‐ion battery cell with a capacity of 25 Ah was cycled under a constant current profile at three different ambient temperatures, and the surface temperature and voltage of the battery were measured. The four machine learning regression models—linear regression, k‐nearest neighbors, random forest, and decision tree—were developed using the scikit‐learn library in Python and validated with experimental data. The results of their performance were reported and compared using the R 2 metric. The decision tree‐based model, with an R 2 score of 0.99, was determined to be the best model in this case study.