A review on intelligent thermal management in flow field with heat transfer for electric vehicles

With the vigorous development of the electric vehicle (EV) industry, the coupling between heat-transfer flow field and thermal management system has become pivotal for enhancing vehicle performance, driving range, and user experience. This review focuses specifically on the interaction mechanism between vehicle heat-transfer flow field and thermal management system, rather than providing broad-ranging coverage of every separate thermal-management sub-topic. Existing evidence indicates that the air-conditioning compressor can consume up to 24% of battery electric energy under extreme ambient conditions, while aerodynamic drag may account for 35% of drive-motor energy loss, highlighting strong coupling between flow-field behaviours and vehicle thermal-energy consumption. This paper delves into the latest research advancements in heat-exchange flow fields and thermal management systems for electric vehicles, adopting combined experimental and simulation perspectives to comprehensively review key components and multi-physics interaction mechanisms covering aerodynamic external flow, power cabin, passenger cabin and underbody flow fields. This review makes three main contributions: first, it systematically analyses the present insufficient coupling between thermal management hardware and heat-transfer flow fields in existing literature; second, it quantitatively discusses bottlenecks of traditional one-dimensional-three-dimensional (1D 3D) co-simulation characterised by long computation cycles; third, it identifies measurable future research targets for intelligent thermal management. We underscore the necessity of multi-physical-field coupled thermal management, and point out that future automotive thermal solutions will move towards integrated on-board controllers aiming to cut hardware costs, simplify wiring and elevate overall system efficiency. To mitigate excessive computation overhead of conventional 1D 3D co-simulation, model-order reduction transferring high-fidelity three-dimensional flow-thermal information into lightweight one-dimensional representations is highlighted to support fast real-time control-strategy adjustment. Moreover, vehicle-level energy co-ordination strategies built upon digital-twin agent frameworks are discussed as emerging technical pathways. This review delivers targeted guidance for advancing EV intelligent thermal management towards high integration, shortened development cycles and reduced system energy consumption.