Battery pack consistency modeling based on generative adversarial networks

被引：29
|
作者：
Fan, Xinyuan [1 ]
Zhang, Weige [1 ]
Sun, Bingxiang [1 ]
Zhang, Junwei [1 ]
He, Xitian [1 ]
机构：
[1] Beijing Jiaotong Univ, Dept Elect Engn, Shang Yuan Cun 3, Beijing 100044, Peoples R China
来源：
ENERGY | 2022年 / 239卷
基金：
中国国家自然科学基金;
关键词：
Energy utilization efficiency; Battery pack consistency; Consistency modeling; Generative adversarial networks; Embedded system; LITHIUM-ION BATTERIES; TO-CELL VARIATION; CYCLE LIFE; CAPACITY; TEMPERATURE;
D O I：
10.1016/j.energy.2021.122419
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
In working condition of battery packs, the battery pack consistency has a great impact on the overall performance of the battery pack. In order to build an accurate battery pack model, we need to build a battery pack consistency model. Firstly, we used a Gaussian mixture model to fit the statistical characteristics of a single parameter. This method can accurately fit the skewness in the parameter distribution and fit the multi-peak characteristics that may appear. Secondly, we constructed a nonparametric battery pack consistency model using a Generative Adversarial Networks (GAN). Our consistency model can accurately describe the statistical characteristics of a single parameter and fits the correlation coefficient between parameters. The battery pack model substituted into the GAN-generated battery parameters exhibits a very high similarity to the experimental data. The relative errors of the simulation results are less than 0.6 % for the terminal voltage and less than 0.3 % for the energy utilization efficiency (EUE), proving the advantages of the GAN consistency model in fitting the distribution of the battery parameters. Finally, we implemented the GAN consistency model in an embedded system with limited computing resources, which proves that our proposed model has the ability to run normally on existing BMS. (C) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页数：13
相关论文
共 50 条
  • [21] Wireless signal enhancement based on generative adversarial networks
    Zhou, Xue
    Sun, Zhuo
    Wu, Hengmiao
    AD HOC NETWORKS, 2020, 103
  • [22] Multisensor Image Fusion based on Generative Adversarial Networks
    Lebedev, M. A.
    Komarov, D., V
    Vygolov, O. V.
    Vizilter, Yu. V.
    IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING XXV, 2019, 11155
  • [23] SSGAN: Secure Steganography Based on Generative Adversarial Networks
    Shi, Haichao
    Dong, Jing
    Wang, Wei
    Qian, Yinlong
    Zhang, Xiaoyu
    ADVANCES IN MULTIMEDIA INFORMATION PROCESSING - PCM 2017, PT I, 2018, 10735 : 534 - 544
  • [24] A New Steganography Method Based on Generative Adversarial Networks
    Naito, Hiroshi
    Zhao, Qiangfu
    2019 IEEE 10TH INTERNATIONAL CONFERENCE ON AWARENESS SCIENCE AND TECHNOLOGY (ICAST 2019), 2019, : 495 - 500
  • [25] Semantic image inpainting based on Generative Adversarial Networks
    Wu, Chugang
    Xian, Yanhua
    Bai, Junqi
    Jing, Yuancheng
    2020 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTER ENGINEERING (ICAICE 2020), 2020, : 276 - 280
  • [26] Test Case Filtering based on Generative Adversarial Networks
    Liu, Zhijuan
    Zhang, Li
    Wu, Xuangou
    Zhao, Wei
    2022 IEEE 23RD INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE SWITCHING AND ROUTING (IEEE HPSR), 2022, : 65 - 69
  • [27] Multimodal Image Fusion Based on Generative Adversarial Networks
    Yang Xiaoli
    Lin Suzhen
    Lu Xiaofei
    Wang Lifang
    Li Dawei
    Wang Bin
    LASER & OPTOELECTRONICS PROGRESS, 2019, 56 (16)
  • [28] Face Image Colorization Based on Generative Adversarial Networks
    Han X.-J.
    Liu Y.-L.
    Yang H.-Y.
    Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology, 2019, 39 (12): : 1285 - 1291
  • [29] Image Hazing Algorithm Based on Generative Adversarial Networks
    Xiao, Jinsheng
    Zhou, Jinglong
    Lei, Junfeng
    Xu, Chuan
    Sui, Haigang
    IEEE ACCESS, 2020, 8 : 15883 - 15894
  • [30] Face attribute editing based on generative adversarial networks
    Song, Xiaoxia
    Shao, Mingwen
    Zuo, Wangmeng
    Li, Cunhe
    SIGNAL IMAGE AND VIDEO PROCESSING, 2020, 14 (06) : 1217 - 1225
12345