基于贝叶斯优化-卷积神经网络-双向长短期记忆神经网络的锂电池健康状态评估

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Abstract Accurate estimation of battery state of health (SOH) is the key to the stable operation of the device. In order to solve the problems in the current SOH research, such as the difficulty to measure the volume directly and the time required to adjust the model parameters, a prediction model based on the multi-health features of Bayesian optimization (BO) optimized convolution neural network (CNN) and bi-directional long short term memory (BiLSTM) neural network is proposed. Based on NASA’s publicly available lithium battery data, three health characteristics are extracted. The combination of CNN and BiLSTM improves the processing ability of time series data, and adds BO algorithm to automatically search the optimal parameter set, which avoids the combination network model falling into the local optimal and reduces the estimation time. The results show that the proposed method has the highest prediction accuracy and can aeffectively estimate the SOH of lithium batteries. The mean absolute error and root mean square error are both within 1%.

Key words： lithium battery state of health (SOH) Bayesian optimization (BO) convolutional neural network (CNN) bi-directional long short term memory (BiLSTM) neural network

Received: 20 January 2024

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	Articles by authors
	YI Sitong
	LIU Yanong
	MA Yaoyi
	LI Wenjie
	KONG Hang

Cite this article:

YI Sitong,LIU Yanong,MA Yaoyi等. State of health assessment of lithium battery based on Bayesian optimization-convolution neural network-bi-directional long short term memory neural network[J]. Electrical Engineering, 2024, 25(5): 1-10.

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https://dqjs.cesmedia.cn/EN/Y2024/V25/I5/1

[1] 熊庆, 邸振国, 汲胜昌. 锂离子电池健康状态估计及寿命预测研究进展综述[J]. 高电压技术, 2024, 50(3): 1182-1195.
[2] 潘锦业, 王苗苗, 阚威, 等. 基于Adam优化算法和长短期记忆神经网络的锂离子电池荷电状态估计方法[J]. 电气技术, 2022, 23(4): 25-30.
[3] KRAUSE F C, RUIZ J P, JONES S C, et al.Performance of commercial Li-ion cells for future NASA missions and aerospace applications[J]. Journal of the Electrochemical Society, 2021, 168(4): 040504.
[4] 邓秉杰, 王伟平, 熊刚. 基于内阻动态电压跟随的锂电池均衡策略[J]. 电气技术, 2023, 24(4): 42-47.
[5] 黄凯, 孙恺, 郭永芳, 等. 基于观测方程重构滤波算法的锂离子电池荷电状态估计[J]. 电工技术学报, 2024, 39(7): 2214-2224.
[6] 崔孝凯, 王庆芝, 刘其朋. 基于数据驱动的锂电池健康状态预测[J/OL]. 复杂系统与复杂性科学, 1-8[2024-1-19]. http://kns.cnki.net/kcms/detail/37.1402.N.20230320.1750.002.html.
[7] 李欣. 车用锂离子电池全生命周期寿命预测与健康管理方法研究[D]. 长春: 吉林大学, 2022.
[8] 姚远, 陈志聪, 吴丽君, 等. 一种基于改进网格搜索和广义回归神经网络的锂离子电池健康状态估计方法[J]. 电气技术, 2021, 22(7): 32-37.
[9] 王聪聪, 吴昊, 裴春兴, 等. CRH380B型动车组镍镉电池健康状态分析[J]. 蓄电池, 2022, 59(1): 1-6.
[10] 傅祎飏, 李锐华, 胡波, 等. 一种参数化电池模拟器模型及参数辨识方法[J]. 电气技术, 2020, 21(11): 7-12.
[11] 潘思源. 基于数据驱动的锂离子电池状态估计及SOP研究[D]. 青岛: 青岛科技大学, 2023.
[12] 顾菊平, 蒋凌, 张新松, 等. 基于特征提取的锂离子电池健康状态评估及影响因素分析[J]. 电工技术学报, 2023, 38(19): 5330-5342.
[13] XIA Zhiyong, ABU QAHOUQ J A. Lithium-ion battery ageing behavior pattern characterization and state-of-health estimation using data-driven method[J]. IEEE Access, 2021, 9: 98287-98304.
[14] 戴国洪, 张道涵, 彭思敏, 等. 人工智能在动力电池健康状态预估研究综述[J/OL]. 机械工程学报, 1-18[2024-01-19]. http://118.89.52.29:8085/kcms/detail/11.2187.TH.20240204.1631.004.html.
[15] 方斯顿, 刘龙真, 孔赖强, 等. 基于双向长短期记忆网络含间接健康指标的锂电池SOH估计[J]. 电力系统自动化, 2024, 48(4): 160-168.
[16] LI Qianglong, LI Dezhi, ZHAO Kun, et al.State of health estimation of lithium-ion battery based on improved ant lion optimization and support vector regression[J]. Journal of Energy Storage, 2022, 50: 104215.
[17] EL-DALAHMEH M, LILLYSTONE J, AL-GREER M, et al.State of health estimation of lithium-ion batteries based on data-driven techniques[C]//International Universities Power Engineering Conference (UPEC), Middlesbrough, United Kingdom, 2021: 1-6.
[18] 周才杰, 汪玉洁, 李凯铨, 等. 基于灰色关联度分析-长短期记忆神经网络的锂离子电池健康状态估计[J]. 电工技术学报, 2022, 37(23): 6065-6073.
[19] 张慧颖, 王军华, 丁汀, 等. 基于多指标的CNN- LSTM锂电池SOH估计模型[J]. 电源技术, 2023, 47(10): 1303-1307.
[20] 付鹏宇. 基于人工智能的锂离子电池健康状态估计[D]. 长春: 吉林大学, 2023.
[21] HUANG Peng, GU Pingwei, KANG Yongzhe, et al.The state of health estimation of lithium-ion batteries based on data-driven and model fusion method[J]. Journal of Cleaner Production, 2022, 366: 132742.
[22] 杨若岑, 冬雷, 廖晓钟, 等. 电池剩余容量估算方法综述[J]. 电气技术, 2019, 20(10): 1-5.