中文核心期刊
CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊

重庆交通大学学报(自然科学版) ›› 2025, Vol. 44 ›› Issue (1): 120-126.DOI: 10.3969/j.issn.1674-0696.2025.01.15

• 交通装备 • 上一篇    

动力电池集成加热系统建模与控制仿真

刘刚,徐健南,陈天宇,张宝徕   

  1. (沈阳航空航天大学 机电工程学院,辽宁 沈阳 110136)
  • 收稿日期:2024-05-21 修回日期:2024-10-20 发布日期:2025-01-20
  • 作者简介:刘 刚(1975—),男,辽宁昌图人,讲师,博士,主要从事为汽车悬架系统理论与控制、新能源汽车与电池技术方面的研究。E-mail:896459071@qq.com

Modeling and Control Simulation of Integrated Heating System for Power Batteries

LIU Gang, XU Jiannan, CHEN Tianyu, ZHANG Baolai   

  1. (School of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, Liaoning, China)
  • Received:2024-05-21 Revised:2024-10-20 Published:2025-01-20

摘要: 为提高车用动力电池在低温环境下的性能,设计了一种以正温度系数热敏电阻(PTC)为主要加热源,空调系统余热作为辅助热源的电池加热系统,基于该系统的传热过程,建立传热数学模型,并利用AMEsim和MATLAB软件建立该系统的仿真模型。电池升温的过程具有延时大、响应慢的特性,为提高系统响应速度,基于模型预测控制算法设计PTC和水泵电机的控制策略。在CLTC-P工况下的仿真结果表明:利用空调余热能提升电池加热速率;与PID控制相比,采用模型预测控制使加热器工作时间减少了106 s,在加热能耗上减少了0.07 kWh。

关键词: 车辆工程;新能源汽车;热管理;电池升温;模型预测控制

Abstract: In order to improve the performance of vehicle power battery in low temperature environment, a battery heating system with positive temperature coefficient (PTC) thermistor as the main heating source and the waste heat of the air conditioning system as the auxiliary heat source was designed. Based on the heat transfer process of the proposed system, a heat transfer mathematical model was established, and AMEsim and MATLAB software were used to establish a simulation model of the proposed system. The process of battery heating had the characteristics of large delay and slow response. In order to improve the response speed of the system, the control strategy of PTC and water pump motor was designed based on the model predictive control algorithm. The simulation results under CLTC-P condition show that the heating rate of the battery can be increased by using the waste heat of the air conditioner. Compared with PID control, the model predictive control reduces the working time of the heater by 106 seconds and the heating energy consumption by 0.07 kWh.

Key words: vehicle engineering; new energy vehicles; thermal management; battery temperature rise; model predictive control

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