Thermal Modeling of Battery Cells for Optimal Tab and Surface Cooling Control
Journal article, 2026

Optimal cooling that minimizes thermal gradients and the average temperature is essential for enhanced battery safety and health. This work presents a new modeling approach for battery cells of different shapes by integrating the Chebyshev spectral-Galerkin (CSG) method and model component decomposition. As a result, a library of reduced-order, computationally efficient battery thermal models is obtained, characterized by different numbers of states. These models are validated against a high-fidelity finite-element model and are compared with a thermal equivalent circuit (TEC) model under real-world vehicle driving and battery cooling scenarios. Illustrative results demonstrate that the proposed model with four states can faithfully capture the 2-D thermal dynamics, while the model with only one state significantly outperforms the widely used two-state TEC model in both accuracy and computational efficiency, reducing computation time by 28.7%. Furthermore, our developed models allow for independent control of tab and surface cooling (SC) channels, enabling effective thermal performance optimization. Additionally, the proposed model's versatility and effectiveness are demonstrated through various applications, including the evaluation of different cooling scenarios, closed-loop temperature control, and the thermal assessment of cell aspect ratio.

control-oriented thermal modeling

Analytical models

Computational modeling

Convection

Cooling

Boundary conditions

spectral method

Thermal management

Battery management system (BMS)

Integrated circuit modeling

Mathematical models

Computational efficiency

Batteries

cooling control

thermal management

Author

Godwin Peprah

Chalmers, Electrical Engineering, Systems and control

Yicun Huang

Chalmers, Electrical Engineering, Systems and control

Uppsala University

Torsten Wik

Chalmers, Electrical Engineering, Systems and control

Faisal Altaf

Volvo Group

Jaguar Land Rover

Changfu Zou

Chalmers, Electrical Engineering, Systems and control

IEEE Transactions on Control Systems Technology

1063-6536 (ISSN) 15580865 (eISSN)

Vol. 34 4 1831-1844

Battery control via adaptive modeling and predictive control

Swedish Research Council (VR) (2019-04873), 2020-01-01 -- 2023-12-31.

E-powertrain predictive maintenance using physics informed learning (TEAMING)

European Commission (EC) (101131278), 2023-12-01 -- 2027-11-30.

Subject Categories (SSIF 2025)

Energy Engineering

Computational Mathematics

Control Engineering

DOI

10.1109/TCST.2026.3675726

More information

Latest update

7/15/2026