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Estimating the Temperature Distribution of Pouch-Type Li-Ion Batteries Based on a Distributed-Parameter Electrothermal Coupling Model

  • Jiale Xie
  • , Mingyu Song
  • , Zongyang Song
  • , Zhongbao Wei
  • , Zhekang Dong*
  • *Corresponding author for this work
  • North China Electric Power University
  • Beijing Institute of Technology
  • Hangzhou Dianzi University

Research output: Contribution to journalArticlepeer-review

Abstract

Pouch-type lithium-ion batteries (PLiBs) often suffer from nonuniform temperature distributions, which accelerate degradation and increase thermal safety risks. To address this, a 2-D electrothermal coupled model is developed by discretizing the cell into grid elements, each represented by a second-order equivalent circuit. Electrothermal interactions are captured through electrical and thermal resistances. Model parameters are identified using a hybrid method that combines parameter mapping with particle swarm optimization, and are validated under dynamic stress test and multirate discharge conditions. Experimental results demonstrate high predictive accuracy, with mean voltage errors below 7.5 mV and temperature deviations within 1 ◦C. The model also successfully reproduces localized hot spots near the current tabs during high-rate discharge. Analysis reveals two coupled causes of thermal nonuniformity: uneven current distribution due to ohmic drops in the current collectors, and heterogeneous heat dissipation across the cell surface. The superposition of these effects exacerbates temperature nonuniformity. This modeling framework offers a robust tool for the design and optimization of battery thermal management systems, providing practical insights into improving safety and extending the service life of large-format PLiBs.

Original languageEnglish
JournalIEEE Transactions on Industrial Electronics
DOIs
Publication statusAccepted/In press - 2026
Externally publishedYes

Keywords

  • Distributed-parameter model
  • electrothermal coupling model
  • pouch-type Li-ion battery
  • temperature distribution estimation

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