[1]王丽玲,孙晓波,宋树平,等.基于 QPSO-LSTM 模型的锂电池剩余容量预测[J].机械与电子,2024,42(09):52-56.
　WANG Liling,SUN Xiaobo,SONG Shuping,et al.Prediction of Remaining Capacity of Lithium Battery Based on QPSO-LSTM Model[J].Machinery & Electronics,2024,42(09):52-56.
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基于 QPSO-LSTM 模型的锂电池剩余容量预测()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
42
期数:
2024年09期
页码:
52-56
栏目:
智能制造
出版日期:
2024-09-27

文章信息/Info

Title:
Prediction of Remaining Capacity of Lithium Battery Based on QPSO-LSTM Model
文章编号:
1001-2257 ( 2024 ) 09-0052-05
作者:
王丽玲 1 ; 孙晓波 1 ; 宋树平 2 ; 张 敬 1 ; 马明叶 1
1. 中国长江电力股份有限公司,湖北 武汉 430050 ;?
2. 常熟理工学院,江苏 苏州 215500
Author(s):
WANG Liling1 ;  SUN Xiaobo2 ;  SONG Shuping2 ;  ZHANG Jing1 ;  MA Mingye1
( 1.China Yangtze Power Co. , Ltd. , Wuhan 430050 , China ;?
2.Changshu Institute of Technology , Suzhou 215500 , China )
关键词:
锂电池; 容量预测; 量子粒子群算法; LSTM 神经网络
Keywords:
lithium battery ;  capacity prediction ;  quantum particle swarm optimization algorithm ;  LSTM neural network
分类号:
TM912
文献标志码:
A
摘要:
为克服锂离子电池容量预测精度低的问题,提出了一种量子粒子群改进长短期记忆神经网络( QPSO-LSTM )的电池容量预测技术。分析了量子粒子群改进( QPSO )和长短期记忆神经网络( LSTM )算法的基本原理,利用 QPSO 算法对 LSTM 模型神经元个数、学习率等主要超参数进行寻优,解决长时序数据预测精度差和预测模型超参数难以确定的问题,构建了 QPSO-LSTM 模型。最后,以 NASA 电池为分析对象,分别采用 QPSO=LSTM 、 PSO=LSTM 、 LSTM 和 GA BP 这 4 种预测模型对 2 种不同型号的电池进行剩余容量预测,预测结果表明, QPSO-LSTM 模型预测精度高,误差在 1.5% 范围内,为电池剩余容量的预测提供了一种有效的方法。
Abstract:
To overcome the problem of low accuracy in predicting lithium-ion battery capacity , a quantum particle swarm optimization improved long short-term memory ( QPSO-LSTM ) neural network battery capacity prediction technology is proposed.Firstly , the basic principles of QPSO and LSTM algorithms are analyzed.Then , to solve the problems of poor prediction accuracy and difficulty in determining hyperparameters for long-term data , the QPSO algorithm is used to optimize the main hyperparameters of the LSTM model , such as the number of neurons and learning rate.The QPSO-LSTM model is constructed. Finally , taking NASA batteries as the analysis object , four prediction models , QPSO-LSTM , PSO-LSTM , LSTM , and GA-BP are used to predict the remaining capacity of two different types of batteries. The prediction results show that the QPSO-LSTM model had high prediction accuracy , with an error within 1.5% , providing an effective method for predicting the remaining capacity of batteries.

参考文献/References:

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相似文献/References:

[1]王 正１,２.基于机器学习的新能源汽车电池剩余寿命预测[J].机械与电子,2019,(12):9.
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[2]王 正１,２.基于DAE-HTPF的新能源汽车电池剩余寿命预测[J].机械与电子,2020,(03):3.
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备注/Memo

备注/Memo:
收稿日期: 2023-12-19
基金项目:长江电力股份有限公司科技项目( 5323020034 )
作者简介:王丽玲 ( 1991- ),女,云南宣威人,工程师,研究方向为电力系统建模与仿真。
更新日期/Last Update: 2024-09-25