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

Fuente: arXiv
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Hauptverfasser: Jahedan, Reihaneh, Peddada, Satya, Jennings, Mark, Katragadda, Sunil, Allison, James, Miljkovic, Nenad
Format: Preprint
Veröffentlicht: 2025
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author Jahedan, Reihaneh
Peddada, Satya
Jennings, Mark
Katragadda, Sunil
Allison, James
Miljkovic, Nenad
author_facet Jahedan, Reihaneh
Peddada, Satya
Jennings, Mark
Katragadda, Sunil
Allison, James
Miljkovic, Nenad
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2511_21855
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Automated Enumeration of Reconfigurable Architectures for Thermal Management Systems in Battery Electric Vehicles
Jahedan, Reihaneh
Peddada, Satya
Jennings, Mark
Katragadda, Sunil
Allison, James
Miljkovic, Nenad
Systems and Control
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.
title Automated Enumeration of Reconfigurable Architectures for Thermal Management Systems in Battery Electric Vehicles
topic Systems and Control
url https://arxiv.org/abs/2511.21855