A Benchmark Suite for Multi-Objective Optimization in Battery Thermal Management System Design
Kaichen Ouyang, Yezhi Xia
TL;DR
This work addresses the need for realistic evaluation of constrained multi-objective optimization in BTMS design by introducing a benchmark suite of 12 RWCMOPs derived from recent BTMS research. Each problem is formulated via surrogate models that approximate high-fidelity CFD or physical simulations, enabling efficient evaluation while capturing key thermal-fluid interactions. The suite targets trade-offs among thermal performance, hydraulic pressure drop, and system weight, with clearly defined decision variables, objectives, and constraints to support robust comparison of CMOEAs. Future directions include establishing baseline results, developing a standardized ranking scheme, expanding the problem set to include emerging BTMS technologies, and integrating the suite with optimization platforms to accelerate advancement in energy storage thermal management optimization.
Abstract
Synthetic Benchmark Problems (SBPs) are commonly used to evaluate the performance of metaheuristic algorithms. However, these SBPs often contain various unrealistic properties, potentially leading to underestimation or overestimation of algorithmic performance. While several benchmark suites comprising real-world problems have been proposed for various types of metaheuristics, a notable gap exists for Constrained Multi-objective Optimization Problems (CMOPs) derived from practical engineering applications, particularly in the domain of Battery Thermal Management System (BTMS) design. To address this gap, this study develops and presents a specialized benchmark suite for multi-objective optimization in BTMS. This suite comprises a diverse collection of real-world constrained problems, each defined via accurate surrogate models based on recent research to efficiently represent complex thermal-fluid interactions. The primary goal of this benchmark suite is to provide a practical and relevant testing ground for evolutionary algorithms and optimization methods focused on energy storage thermal management. Future work will involve establishing comprehensive baseline results using state-of-the-art algorithms, conducting comparative analyses, and developing a standardized ranking scheme to facilitate robust performance assessment.