基于双卡尔曼滤波算法的动力电池内部温度估计

Translated title of the contribution: Battery Internal Temperature Estimation Method through Double Extended Kalman Filtering Algorithm

Rui Xiong*, Xinggang Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

Accurate battery internal temperature is very important to improve the safety and reliability of battery applications. However, due to many factors such as sensors and testing methods, its internal temperature is difficult to measure online. After integrating the Bernardi battery heat generation model and heat transfer model, the internal and external temperature of the battery is expressed using the equation of state analysis to obtain a discrete-time system of temperature; the double extended Kalman filter is used to establish the real-time temperature and environmental parameters of the battery. The estimation model realizes online estimation of the internal temperature of the battery. Results of the battery through the built-in temperature sensor show that the method can estimate the internal temperature of online with an error of <1℃ and high accuracy.

Translated title of the contributionBattery Internal Temperature Estimation Method through Double Extended Kalman Filtering Algorithm
Original languageChinese (Traditional)
Pages (from-to)146-151
Number of pages6
JournalJixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering
Volume56
Issue number14
DOIs
Publication statusPublished - 20 Jul 2020

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