电源学报2025，Vol.23Issue(2)：240-246,7.DOI:10.13234/j.issn.2095-2805.2025.2.240
基于预测控制的电动汽车快充模块热管理策略
Thermal Management Strategy for Electric Vehicle Fast Charging Module Based on Predictive Control
摘要
Abstract
Electric vehicle fast charging piles are prone to overheating of power devices under high-power operation,causing potential safety hazards.However,the existing cooling strategy adopts a rule-based forced air cooling method,and the cooling fan rotates at a high speed and generates large environmental noise.To protect the thermal safety of core components in the module while optimizing the cooling regulation strategy,an optimal thermal management method for electric vehicle fast charging module based on data-driven model predictive control(MPC)is proposed.This method adopts a data-driven method to construct a prediction model of module temperature distribution based on the long short-term memory neural network,and it combines MPC to control the fan speed,thus optimizing the thermal management strategy for the fast charging module and reducing the fan noise.Through experimental tests,it was verified that this method can effectively reduce the average fan speed by 1 293 rpm and reduce the average noise by 4.99 dB while ensuring that the key components are not overheated,which ensures the thermal safety of core components and the durability of the cooling fan.关键词
模型预测控制/长短期记忆神经网络/快充模块/热管理/风扇降噪Key words
Model predictive control(MPC)/long short-term memory neural network/fast charging module/thermal management/fan noise reduction分类
动力与电气工程引用本文复制引用
李靖璇,鲁岩松,朱翀,卢徐,张希..基于预测控制的电动汽车快充模块热管理策略[J].电源学报,2025,23(2):240-246,7.基金项目
国家自然科学基金资助项目(52177218,52007119) （52177218,52007119）
科技部重点研发计划资助项目(2019YFE0100200)This work is supported by National Natural Science Foundation of China under the grant 52177218 and 52007119 （2019YFE0100200）
National Key R&D Plan Key Special Project under the grant 2019YFE0100200 （）
