A real-time feedback and adaptive control strategy for battery thermal management system

Xinrui Fang, Haonan Tian, Muyao Wu, Yangrui Qiu, Heng Li and Li Wang

Energy, 2025, vol. 333, issue C

Abstract: The control of a battery thermal management system (BTMS) is critical for ensuring thermal safety, energy efficiency, and prolonged service life of electric vehicles. To explore the coordination optimization between battery thermal safety and comprehensive energy consumption, this paper proposes a real-time feedback and adaptive control strategy based on Adaptive Model Predictive Control (AMPC). First, a computational fluid dynamics (CFD) model of the battery pack is established and verified under dynamic operating conditions and temperature variations. In the proposed real-time AMPC framework, accurate feedback of the battery temperature distribution is provided by real-time multiphysics simulations. The thermal parameters of the battery are adaptively updated online to precisely predict battery thermal states. A cost function consisting of the battery temperature, battery aging cost, and cooling cost is minimized to obtain the optimal flow velocity of the liquid cooling system. The results show that the proposed method reduces cooling energy consumption by 26.9%,limits temperature fluctuations to between 0.031 and 0.040 K, and reduces battery aging rate by up to 22%.

Keywords: Electric vehicles; Battery thermal management; Thermal safety; AMPC (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:333:y:2025:i:c:s0360544225027902

DOI: 10.1016/j.energy.2025.137148

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