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.

翻译：合成基准问题（SBPs）常用于评估元启发式算法的性能。然而，这些SBPs通常包含各种不切实际的特性，可能导致对算法性能的低估或高估。尽管已有针对多种元启发式算法提出的包含实际问题的基准测试套件，但在源自实际工程应用的约束多目标优化问题（CMOPs）领域，特别是在电池热管理系统（BTMS）设计中，仍存在显著空白。为填补这一空白，本研究开发并提出了一个专门用于BTMS多目标优化的基准测试套件。该套件包含一系列多样化的实际约束问题，每个问题均基于近期研究通过精确的代理模型定义，以高效表征复杂的热-流体相互作用。该基准测试套件的主要目标是为专注于储能热管理的进化算法和优化方法提供一个实用且相关的测试平台。未来工作将包括使用先进算法建立全面的基线结果、进行对比分析，并开发标准化的排名方案，以促进稳健的性能评估。