新能源汽车热管理系统协同优化策略

何亚茹; 梁坤峰; 高春艳; 陈浩远; 李琳琳; 李硕鹏; 陈彬; 王林; 谈莹莹

doi:10.7652/xjtuxb202602005

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新能源汽车热管理系统协同优化策略

专题新能源电动车辆热管理 | 更新时间：2026-02-09

- 新能源汽车热管理系统协同优化策略
- Collaborative Optimization Strategy for Thermal Management System of New Energy Vehicles
- 西安交通大学学报 2026年60卷第2期页码：49-58
- 作者机构：
  
  河南科技大学车辆与交通工程学院，471003，河南洛阳
  豫新汽车热管理科技有限公司，453000，河南新乡
  河南科技大学土木建筑学院，471023，河南洛阳
- 作者简介：
  
  作者简介：何亚茹（2001—），女，硕士生；
  梁坤峰（通信作者），男，教授，博士生导师。
- 基金信息：
  
  国家自然科学基金资助项目(52378094);洛阳市科技发展计划资助项目。(2302035A)
- DOI：10.7652/xjtuxb202602005
  中图分类号： TB66
- 收稿：2025-07-31，
  
  纸质出版：2026-02-10
- 稿件说明：
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何亚茹, 梁坤峰, 高春艳, 等. 新能源汽车热管理系统协同优化策略[J]. 西安交通大学学报, 2026,60(2):49-58.

HE Yaru, LIANG Kunfeng, GAO Chunyan, et al. Collaborative Optimization Strategy for Thermal Management System of New Energy Vehicles[J]. Journal of Xi'an Jiaotong University, 2026, 60(2): 49-58.
何亚茹, 梁坤峰, 高春艳, 等. 新能源汽车热管理系统协同优化策略[J]. 西安交通大学学报, 2026,60(2):49-58. DOI： 10.7652/xjtuxb202602005.

HE Yaru, LIANG Kunfeng, GAO Chunyan, et al. Collaborative Optimization Strategy for Thermal Management System of New Energy Vehicles[J]. Journal of Xi'an Jiaotong University, 2026, 60(2): 49-58. DOI： 10.7652/xjtuxb202602005.

摘要

针对新能源汽车热管理系统多目标协同的调控需求，提出了一种协同控制策略。首先，基于AMESim平台，构建乘员舱-电池耦合热系统动态仿真模型；其次，将非线性自回归外生输入神经网络引入控制模块，模型预测控制（MPC）以降低压缩机能耗和控制最小化温度偏差为双目标，优化压缩机转速；接着，对阀件等关键执行机构建立基于比例-积分-微分（PID）的动态响应模型，以实现制冷剂流量的分配；最后，将热管理系统模型与MPC控制模型进行联合仿真，在中国轻型汽车测试循环-乘用车工况中对比分析了MPC与PID控制策略对电池和乘员舱温度动态响应的调控效果。研究结果表明：环境温度为35℃时，相较于PID控制，MPC策略能将乘员舱温度的超调量降低0.9℃，且在环境温度为40、45℃时均未出现超调现象；与PID控制相比，当环境温度为35、40、45℃时，一个循环工况下采用MPC策略的压缩机平均温降能耗分别降低了7.2%、2.4%和3.5%，整体能效表现优于PID控制。该研究为实现整车热管理提供了可靠的控制方法，对新能源汽车优化研究具有较好的参考价值。

Abstract

To address the multi-obj ective collaborative control requirements of the thermal management system in new energy vehicles，a coordinated control strategy is proposed.First，a dynamic simulation model of the passenger compartment-battery coupled thermal system was constructed based on the AMESim platform.Second，a nonlinear autoregressive exogenous input neural network was introduced into the control module，and a model predictive controller（MPC）was designed to optimize compressor speed with the dual obj ectives of reducing compressor energy consumption and minimizing temperature deviation.Subsequently，a dynamic response model based on proportional-integral-derivative（PID）control was established for key actuators such as valves to achieve refrigerant flow distribution.Finally，a co-simulation of the thermal management system model and the MPC control model was performed，and the regulation effects of MPC and PID control strategies on the dynamic temperature responses of the battery and passenger compartment were compared and analyzed under the China light-duty vehicle test cycle-passenger condition.The results show that at an ambient temperature of 35℃，compared with PID control，the MPC strategy reduces the overshoot of the passenger compartment temperature by 0.9℃，and no overshoot occurs at ambient temperatures of 40 and 45℃.Compared with PID control，at ambient temperatures of 35，40，and 45℃，the average energy consumption for compressor temperature reduction per cycle under the MPC strategy is reduced by 7.2%，2.4%，and 3.5%，respectively，indicating overall energy efficiency superior to that of PID control.This study provides a reliable control method for vehicle thermal management and offers valuable reference for optimization research on new energy vehicles.

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references

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