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

作　　者：李靖璇 鲁岩松 朱翀 卢徐 张希[1] LI Jingxuan;LU Yansong;ZHU Chong;LU Xu;ZHANG Xi(School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海交通大学机械与动力工程学院,上海200240

出　　处：《电源学报》2025年第2期240-246,共7页Journal of Power Supply

基　　金：国家自然科学基金资助项目(52177218,52007119);科技部重点研发计划资助项目(2019YFE0100200)。

摘　　要：电动汽车快充充电桩在高功率工作下功率器件容易超温造成安全隐患,而现有冷却策略采用基于规则控制的强制风冷方式,散热风扇转速大且产生较大的环境噪声。为保护模块核心器件的热安全同时优化冷却调节策略,提出1种基于数据驱动模型预测控制MPC(model predictive control)的电动汽车快充模块优化热管理方法。该方法采用数据驱动构建基于长短期记忆神经网络的模块温度分布的预测模型,并结合MPC对风扇转速进行调控,优化快充模块热管理策略并降低风扇噪声。经过实验测试,验证了该方法在保证各关键器件不超温的同时,可有效降低风扇平均转速1 293 rpm,降低平均噪声4.99 dB,保障了核心器件热安全性及散热风扇耐久性。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 1293 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 

分 类 号：TM910.6[电气工程—电力电子与电力传动]