Automated Enumeration of Reconfigurable Architectures for Thermal Management Systems in Battery Electric Vehicles

TL;DR

This paper introduces a systematic, automated approach to enumerate, simulate, and optimize reconfigurable thermal management system architectures for electric vehicle batteries, enhancing design efficiency and performance trade-offs.

Contribution

It presents a graph-based modeling method and automation framework for exploring and optimizing multiple TMS architectures in BEVs, a novel holistic approach in this domain.

Findings

Explored over 150 mode sequences, identifying 39 unique architectures.

Automated MATLAB Simscape models created for performance evaluation.

Multi-objective optimization supports decision-making based on user priorities.

Abstract

As the automotive industry moves towards vehicle electrification, designing and optimizing thermal management systems (TMSs) for Battery Electric Vehicles (BEVs) has become a critical focus in recent years. The dependence of battery performance on operating temperature, the lack of waste combustion heat, and the significant effect of TMS energy consumption on driving range make the design of BEV TMSs highly complicated compared to conventional vehicles. Although prior research has focused on optimizing the configuration of thermal systems for varying ambient conditions, a holistic approach to studying the full potential of reconfigurable TMS architectures has not yet been fully explored. The complex design landscape of multi-mode reconfigurable systems is difficult to navigate. Relying solely on expert intuition and creativity to identify new architectures both restricts progress and leaves significant performance improvements unrealized. In this study, using graph modelling of TMS architectures, we propose a systematic method to automatically enumerate and simulate reconfigurable architectures for a TMS, given the desired operating modes, along with a framework to conduct transient performance analysis and optimization-based trade-off studies among system performance, energy consumption, and complexity. We explored more than 150 operating mode sequences, retaining 39 unique architectures for further evaluation. MATLAB Simscape models of these architectures were automatically created and their performance evaluated. The multi-objective optimization results provide decision support for selecting the best architecture based on user priorities.