Physics-Informed Machine Learning for Pouch Cell Temperature Estimation

2026-04-16 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

A new physics-informed machine learning (PIML) framework has been developed for estimating steady-state temperature profiles in pouch cells with indirect liquid cooling, crucial for optimizing battery thermal management in electric vehicles. This approach embeds governing heat transfer equations directly into a neural network's loss function, allowing for high-fidelity predictions. The PIML model demonstrates significantly faster convergence and superior accuracy compared to purely data-driven methods, achieving a 49.1% reduction in mean squared error. Validation on independent test cases confirms its enhanced performance, particularly in areas distant from cooling channels, highlighting its potential for surrogate modeling and design optimization in battery systems.

Key takeaway

For Machine Learning Engineers developing battery thermal management systems, this PIML framework offers a robust solution for accurate temperature estimation. You should consider integrating governing physics equations into your neural network loss functions to achieve faster convergence and significantly higher accuracy, especially in complex thermal environments. This approach can lead to more reliable surrogate models and optimized battery designs.

Key insights

PIML integrates physics equations into neural network loss for accurate, efficient temperature estimation in battery cells.

Principles

• Physics-informed models converge faster than data-driven ones.
• Integrating physical laws enhances model accuracy and reliability.

Method

The PIML framework incorporates heat transfer equations into the neural network's loss function to guide learning and improve prediction fidelity for steady-state temperature profiles.

In practice

• Optimize battery thermal management systems.
• Improve surrogate modeling for battery design.

Topics

• Physics-Informed Machine Learning
• Pouch Cell Temperature Estimation
• Battery Thermal Management
• Heat Transfer Equations
• Surrogate Modeling

Code references

• bu32loxa/PiGRAND

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.