• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2021, Vol. 57 ›› Issue (14): 1-9.doi: 10.3901/JME.2021.14.001

• 特邀专栏:电源系统设计、管理与大数据 •    下一篇

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考虑混杂充电数据的锂离子电池容量估计

周子游1,2, 刘永刚1,2, 杨阳1,2, 陈峥3, 舒星3, 秦大同1,2   

  1. 1. 重庆大学机械与运载工程学院 重庆 400044;
    2. 重庆大学机械传动国家重点实验室 重庆 400044;
    3. 昆明理工大学交通工程学院 昆明 650500
  • 收稿日期:2020-12-12 修回日期:2021-02-24 出版日期:2021-09-15 发布日期:2021-09-15
  • 通讯作者: 刘永刚(通信作者),男,1982年出生,博士,教授,博士研究生导师。主要研究方向为新能源汽车动力系统优化与控制、智能汽车决策与控制技术、车辆自动变速传动及综合控制。E-mail:andyliuyg@cqu.edu.cn
  • 作者简介:周子游,男,1997年出生。主要研究方向为电池能量管理。E-mail:zhouziyou@cqu.edu.cn;杨阳,男,1958年出生,博士,教授,博士研究生导师。主要研究方向为车辆混合动力传动、车辆机电液一体化控制。E-mail:yangyang@cqu.edu.cn;陈峥,男,1982年出生,博士,教授,博士研究生导师。主要研究方向为动力电池管理、智能车辆控制及混合动力汽车能量管理。E-mail:chen@kust.edu.cn;舒星,男,1992年出生,博士研究生。主要研究方向为动力电池管理。E-mail:shuxing92@kust.edu.cn;秦大同,男,1956年出生,博士,教授,博士研究生导师。主要研究方向为机械传动系统、车辆动力传动及其综合控制。E-mail:dtqin@cqu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(51775063)

Capacity Estimation of Lithium Ion Battery Considering Hybrid Charging Data

ZHOU Ziyou1,2, LIU Yonggang1,2, YANG Yang1,2, CHEN Zheng3, SHU Xing3, QIN Datong1,2   

  1. 1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044;
    2. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044;
    3. Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500
  • Received:2020-12-12 Revised:2021-02-24 Online:2021-09-15 Published:2021-09-15

摘要: 准确有效的电池容量估计对于电动汽车的安全性等有着十分重要的意义。目前结合健康因子提取的电池容量估计方法受到了广泛的关注,然而大多数研究没有考虑到电池实际应用中每个循环的充电数据会根据充放电情况的不同而具有不同的充电数据结构,这会导致健康因子的提取不能连续有效地进行,无效或缺失的健康因子序列会导致无法有效地估计电池容量,由此开展考虑混杂充电数据的锂离子电池容量估计方法研究。考虑三种最常见的充电数据结构组成混杂充电数据,根据不同的数据结构提取有效健康因子,再由粒子群算法寻优获得最佳健康因子;以相关向量回归为工具,通过健康因子估计健康因子的方法获取其中一种完整健康因子序列;以完整的健康因子序列训练长短时记忆网络以达到估计未来电池容量的目的。仿真试验结果表明,RVM估计健康因子的相对误差均保持在1%以内,未来电池容量的估计相对误差基本在2%以内,达到较高的估计精度,可满足一定的实际应用需求。

关键词: 锂离子电池, 电池容量估计, 混杂充电数据, 健康因子, 数据驱动模型

Abstract: Accurate and effective battery capacity estimation is very important for the electric vehicles' safety. Currently, the battery capacity estimation method combined with health factors extraction has received widespread attention, however, most studies fail to take into account that the charging data of each cycle in the actual application of batteries will have different charging data structures according to the different charging and discharging situations, which will lead to the inability to continuously and effectively extract health factors, invalid or missing health factor sequences would fail to effectively estimate battery capacity, so a capacity estimation method for lithium-ion batteries considering hybrid charging data is carried out. First, three most common charging data structures are considered to form the hybrid charging data. Effective health factors are extracted according to different data structures, and then particle swarm optimization algorithm is used to obtain the best health factors. Second, the complete health factor sequences are obtained by using the method of health factor estimation based on the relevance vector regression(RVM). Third, the complete health factor sequences are used to train the LSTM to estimate the future battery capacity. The simulation result shows that the relative errors of RVM estimation of health factors are kept within 1%, and the relative errors of future battery capacity are basically kept within 2%, which achieves high accuracy and may meets certain practical application requirements.

Key words: lithium-ion batteries, battery capacity estimation, hybrid charging data, health factors, data-driven model

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