汽车工程 ›› 2025, Vol. 47 ›› Issue (6): 1037-1047.doi: 10.19562/j.chinasae.qcgc.2025.06.003

• • 上一篇    

车云协同下动力电池多级安全管理方法研究

成林穗1,2,3,杨继斌1,2,3(),邓鹏毅1,2,3,武小花1,2,3,徐晓惠1,2,3,邓柯1,2,3   

  1. 1.西华大学,汽车测控与安全四川省重点实验室,成都 610039
    2.西华大学,四川省新能源汽车智能控制与仿真测试技术工程研究中心,成都 610039
    3.宜宾西华大学研究院,宜宾 644000
  • 收稿日期:2024-08-26 修回日期:2024-11-03 出版日期:2025-06-25 发布日期:2025-06-20
  • 通讯作者: 杨继斌 E-mail:yangjibin08@163.com
  • 基金资助:
    第二十七届中国科协年会学术论文。四川省科技计划项目(2025ZNSFSC0427);四川省宜宾市产教融合项目(YB-XHU-20240001)

Research on Multi-level Safety Management Method of Power Battery Under Vehicle-Cloud Collaboration

Linsui Cheng1,2,3,Jibin Yang1,2,3(),Pengyi Deng1,2,3,Xiaohua Wu1,2,3,Xiaohui Xu1,2,3,Ke Deng1,2,3   

  1. 1.Xihua University,Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province,Chengdu 610039
    2.Xihua University,Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan,Chengdu 610039
    3.Yibin Institute in Xihua University,Yibin 644000
  • Received:2024-08-26 Revised:2024-11-03 Online:2025-06-25 Published:2025-06-20
  • Contact: Jibin Yang E-mail:yangjibin08@163.com

摘要:

为解决新能源汽车动力电池的热失控安全问题,本文提出了一种车云协同的多时间尺度动力电池安全管理方法。在短时间尺度上,车端电池管理系统采用值-率模型和电压二维故障特征提取,实现对电池包异常值和不一致性的实时诊断与报警。长时间尺度上,云端利用大量历史数据和车端预警信息,结合改进香农熵、多尺度模糊熵和异常系数的方法,基于传入数据进行热失控的趋势预测。最后,基于实际热失控车辆的历史数据验证表明,该方法在云端至少提前5天发出预警,实现动力电池车-云两端长短时域协同多级管理。

关键词: 动力电池, 热失控, 车云协同, 电池管理, 多熵融合

Abstract:

In order to solve the problem of thermal runaway safety of power batteries in new energy vehicles, in this paper a multi time scale safety management method for batteries based on vehicle-cloud collaboration. On the short time scale, the value-rate model and two-dimensional fault feature are employed for the real-time diagnosis of outliers and inconsistencies pertaining to battery packs on the vehicle-end battery management system. On the long-timescale, the cloud utilizes a substantial quantity of historical data and information about vehicle-end warnings, combined with improved Shannon entropy, multi-scale fuzzy entropy, and anomaly coefficient methods, to predict the trend of the thermal runaway based on incoming data. Finally, validation based on historical data from actual thermal runaway vehicles shows that the proposed method can issue early warnings on the cloud at least five days in advance, achieving multi-level management of the long and short time domain collaboration between the vehicle and cloud ends of power batteries.

Key words: power batteries, thermal runaway, vehicle-cloud collaboration, battery management, multi-entropy fusion