电动汽车电池热管理系统的机器学习建模与优化综述OA

The battery thermal management system(BTMS)is a key subsystem that ensures the safe operation,energy efficiency,and longevity of traction batteries in electric vehicles.Traditional physics-based methods rely on fine-grained modeling and parameter tuning,and under complex operating conditions they often suffer from model mismatch,limited real-time performance,and high computational cost.With advances in sensing and computing,machine learning has been widely applied to thermal-state modeling,thermal anomaly detection,and cooling-control optimization,and has also driven the adoption of lightweight techniques,such as model compression,quantization,and distillation on in-vehicle embedded platforms.This paper systematically reviews related progress of machine learning in thermal state modeling and prediction:Supervised learning shows clear advantages in temperature prediction,unsupervised and semi-supervised learning per-form well in thermal anomaly detection,and reinforcement learning exhibits adaptive strengths in optimizing cooling-control strategies.By comparing trade-offs among accuracy,real-time capability,and computational demand,this paper emphasizes that physics-data integration and lightweight implementation are pivotal for engineering deployability.Over-all,machine learning complements traditional approaches,improves prediction and control performance,and provides guidance for the intelligent development and engineering application of BTMS.