Open Access

ARTICLE

Numerical Analysis and Multi-Objective Optimization of Tesla Valve Cold Plates for Lithium-Ion Battery Thermal Management

Anjie Hu¹, Rui Zhao¹, Liu Tang^2,3,*, Jun Wang^2,3, Dong Liu¹

1 School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang, China
2 Sichuan Province Engineering Technology Research Center of Healthy Human Settlement, Chengdu, China
3 Sichuan University Engineering Design & Research Institute Co., Ltd., Chengdu, China

* Corresponding Author: Liu Tang. Email:

(This article belongs to the Special Issue: Fluid Mechanics & Thermodynamics in Renewable Energy and HVAC Systems)

Fluid Dynamics & Materials Processing 2026, 22(5), 4 https://doi.org/10.32604/fdmp.2026.080001

Received 01 February 2026; Accepted 28 April 2026; Issue published 27 May 2026

Abstract

Lithium-ion batteries are widely deployed in electric vehicles, yet their performance and safety are strongly constrained by elevated operating temperatures, which may accelerate degradation and, in extreme cases, trigger thermal runaway. This study numerically investigates the thermal performance of a Tesla valve-based cold plate for battery thermal management, with the aim of enhancing heat dissipation efficiency through multi-parameter collaborative optimization. An initial screening is conducted using orthogonal experimental design to evaluate the effects of shunt angle (30°–50°), number of unit pairs (3–7), channel asymmetry ratio (0–0.5), and branch channel width (2–4 mm) on maximum temperature difference and pressure drop. The results indicate that the number of unit pairs and the asymmetry ratio are the dominant factors governing thermal and hydraulic performance. To further quantify these relationships, an optimal Latin hypercube sampling strategy is combined with Kriging surrogate modeling to construct response surfaces linking design variables to system performance. Subsequently, a multi-objective optimization based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) genetic algorithm is performed to simultaneously minimize temperature non-uniformity and pressure drop, yielding a Pareto-optimal solution set. The optimal configuration corresponds to a shunt angle of 30°, 7 unit pairs, a zero asymmetry ratio, and a branch channel width of 4 mm. Compared with the baseline design, this configuration reduces the maximum temperature difference by 9.13% and the average temperature difference by 15.03%, while also decreasing pressure drop by 0.36%.

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Keywords

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