A Benchmark Suite for Multi-Objective Optimization in Battery Thermal Management System Design

2025-10-29

AI Summary

Existing synthetic benchmark problems (SBPs) lack realistic engineering constraints, limiting their ability to accurately evaluate metaheuristic algorithms for battery thermal management system (BTMS) design. To address this, we propose CMOP-BTMS—the first constrained multi-objective optimization benchmark suite specifically tailored for BTMS. It is grounded in high-fidelity thermo-fluid coupling physics, employs a CFD-driven surrogate model to capture complex thermal dynamics with high accuracy, and incorporates key engineering constraints including energy efficiency, temperature uniformity, and pressure drop. The suite spans representative BTMS configurations and operational conditions, enabling reproducible and comparable assessment of algorithmic trade-off capabilities across multiple objectives. Its core innovation lies in the integration of physically grounded modeling with standardized benchmark design—filling a critical gap in engineering-oriented optimization algorithm validation platforms—and substantially enhancing the practicality and reliability of algorithm evaluation.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Constraint Satisfaction and Optimization: Constraint Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Addresses unrealistic properties in synthetic optimization benchmarks
• Fills benchmark gap for constrained multi-objective BTMS problems
• Provides real-world testing ground for battery thermal algorithms

Innovation

• Developed surrogate models for thermal-fluid interactions
• Created real-world constrained multi-objective optimization problems
• Established benchmark suite for battery thermal management