[1]刘 丹,刘宇豪. 基于贝叶斯优化与LSTM 的电动汽车动力电池热控制系统设计[J].机械与电子,2026,44(01):120-126.
　LIU Dan,LIU Yuhao. Thermal Management System Design of Electric Vehicle Power Battery Based on Bayesian Optimization and LSTM[J].Machinery & Electronics,2026,44(01):120-126.
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 基于贝叶斯优化与LSTM 的电动汽车动力电池热控制系统设计()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
44
期数:
2026年01期
页码:
120-126
栏目:
电力控制
出版日期:
2026-01-27

文章信息/Info

Title:
 Thermal Management System Design of Electric Vehicle Power Battery Based on Bayesian Optimization and LSTM
文章编号:
1001-2257(2026)01-0120-07
作者:
 刘 丹1; 刘宇豪2
 (1.陕西国防工业职业技术学院,陕西 西安 710300;
2.摩尔线程智能科技(北京)股份有限公司,陕西 西安 710000)
Author(s):
 LIU Dan1; LIU Yuhao2
 (1.Shaanxi National Defense Industry Vocational Technical College,Xi’an 710300,China;
2.Moore Threads Technology (Beijing) Co.,Ltd.,Xi’an 710000,China)
关键词:
 ; 动力电池; 热控制; LSTM 网络; 贝叶斯优化; 多头注意力机制
Keywords:
power battery; thermal control; LSTM network; Bayesian optimization; multi head attentionmechanism
分类号:
U469.72;TP273
文献标志码:
A
摘要:
 针对电动汽车动力电池热控制系统中的热时滞效应且非线性耦合变量复杂、动态工况适应性不足等问题,提出一种基于贝叶斯优化结合长短期记忆网络的热控制方法。构建2层LSTM 网络并引入多头注意力机制,以历史温度序列、动力电池电流、环境温度、冷却液流量及PTC加热器功率作为输入,对动力电池温度进行精准预测并优化热控制策略。利用贝叶斯优化算法对LSTM 网络超参数进行自动寻优。基于动力电池运行数据设计仿真实验,实验结果中所提方法的决定系数(R2)标准差为0.005,均方根误差为0.225 ℃。相较于对比方法,所提方法在温度预测的稳定性与精确性方面均显著提升。
Abstract:
 To address the challenges in thermal management systems for electric vehicle power batteries,such as thermal hysteresis effects,complex nonlinear coupling variables,and inadequate adaptability under dynamic operating conditions,this study proposes a thermal control strategy integrating Bayesian optimization with a long short term memory (LSTM) network.A two layer LSTM network incorporating multi head attention mechanism is constructed,which uses historical temperature series,power battery current,ambient temperature,coolant flow rate and PTC heater power as inputs,so that to predict the battery temperature and optimize the thermal control strategy accurately.Hyperparameters of the LSTM network is automatically tuned using the Bayesian optimization algorithm.Based on the operational data of power battery,simulation experiments demonstrate that the proposed method achieves a coefficient of determination (R2) standard deviation of 0.005,a root mean square error (RMSE) of 0.225 ℃.Compared with the benchmark method,the stability and accuracy of the proposed method are significantly improved.

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备注/Memo

备注/Memo:
 收稿日期:2025-09-26
基金项目:陕西国防工业职业技术学院2025年度科研项目(Gfy25-15)
作者简介:刘 丹 (1993-),女,陕西咸阳人,硕士,讲师,研究方向为电池热管理,通信作者,E mail:976538063@qq.com。
更新日期/Last Update: 2026-04-27