Thermal management systems for battery electric vehicles: A comprehensive review and control-based classification

Abstract

An efficient vehicle thermal management system (VTMS) is essential for ensuring the performance, safety, and driving range of battery electric vehicles (BEVs), and for supporting vehicle-level range performance under representative operating conditions. Maintaining all major thermal subsystems within their optimal temperature ranges is therefore critical to overall vehicle performance and market competitiveness. Over the past decade, VTMS technologies for BEV applications have been extensively investigated. However, existing reviews primarily focus on system configurations, heat-transfer mechanisms, and material properties, with limited systematic analysis from a control-oriented perspective to provide unified theoretical guidance. Therefore, this paper presents a comprehensive review of VTMS control methods for BEVs from a control-theoretical viewpoint. Different from the conventional active/passive/hybrid hardware classification, the proposed control-oriented taxonomy classifies VTMS methods as preventive and corrective strategies according to intervention timing, information source, and dominant control mechanism. Preventive control methods, based on feedforward control principles, mitigate thermal issues through geometric optimization, parametric optimization, and hybrid optimization approaches. Corrective control methods, grounded in feedback concepts, achieve real-time thermal deviation correction through classical, modern, and intelligent control techniques. Comparative studies reveal that preventive control methods offer advantages including high design flexibility, superior temperature uniformity, and mature commercialization, but are constrained by high manufacturing costs, lengthy validation cycles, and increased system weight. Corrective control methods demonstrate excellent performance in battery-pack compatibility, adaptability to complex operating conditions, and multi-subsystem coordination, though their development is limited by computational burden and algorithmic complexity. Intelligent control methods exhibit significant advantages in multi-objective optimization and thermal safety assurance, and show strong potential for improving vehicle-level energy efficiency and driving-range performance under application-specific operating conditions, representing an important future direction of VTMS development. The proposed control-oriented classification framework provides unified theoretical guidance for the design, optimization, and control of BEV VTMS, thereby supporting the coordinated improvement of thermal safety, energy efficiency, and vehicle-level range performance under clearly defined operating conditions.