汽车工程

汽车工程 ›› 2019, Vol. 41 ›› Issue (1): 1-6.doi: 10.19562/j.chinasae.qcgc.2019.01.001

• •    下一篇

基于恒流充电曲线电压特征点的锂离子电池自适应容量估计方法*

来鑫1, 秦超1, 郑岳久1,2, 韩雪冰2   

  1. 1.上海理工大学机械学院,上海 200093;
    2.清华大学,汽车安全与节能国家重点实验室,北京 100084
  • 收稿日期:2018-07-17 出版日期:2019-01-25 发布日期:2019-01-25
  • 通讯作者: 韩雪冰,博士,特别研究员,E-mail:hanxuebing@mail.tsinghua.edu.cn
  • 基金资助:
    *国家自然科学基金(51507102,51877138)和上海市教育发展基金会“晨光计划”(16CG52)资助。

An Adaptive Capacity Estimation Scheme for Lithium-ion Battery Basedon Voltage Characteristic Points in Constant-current Charging Curve

Lai Xin1, Qin Chao1, Zheng Yuejiu1,2, Han Xuebing2   

  1. 1.College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093;
    2.Tsinghua University, State Key Laboratory of Automotive Energy and Safety, Beijing 100084
  • Received:2018-07-17 Online:2019-01-25 Published:2019-01-25

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摘要： 为提高锂离子电池容量在线估计精度,本文中提出一种基于部分充电曲线特征容量在线辨识和阿伦尼乌斯容量衰减模型融合的自适应容量估计方法。针对纯电动汽车极少存在完整充电的情况,提出一种基于恒流充电电压特征点的容量在线辨识方法。该方法先利用遗传算法对缩放平移后的充电曲线进行电压特征点优化,再通过监测有关这两个不动的电压特征点的恒流充电数据,在线辨识电池的当前容量。为进一步提高容量在线估计的精度,通过增量式PID算法来融合容量在线辨识值和阿伦尼乌斯模型,进行模型参数的闭环修正。最后,交变温度寿命实验结果表明,利用本文中提出的自适应估计方法,最大估计误差不超过2%。

关键词: 容量估计, 循环寿命, 阿伦尼乌斯模型, 模型参数, 增量式PID

Abstract: To improve the online capacity estimation accuracy of lithium-ion batteries, an adaptive capacity estimation scheme combining the online capacity identification based on the features of a few charging curves with Arrhenius capacity decay model is proposed. In view of the seldom situations of complete charging in battery electric vehicles, an online capacity identification scheme based on voltage characteristic points of constant-current charging curves is put forward. The scheme uses genetic algorithm to optimize the voltage characteristic points of the scaled and translated charging curves first and then online identify the present capacity of the battery by monitoring the constant-current charging data regarding the two fixed voltage characteristic points. For further enhancing the accuracy of online capacity estimation, an incremental PID algorithm is used to fuse the online capacity estimation and Arrhenius model to perform the closed-loop correction of model parameters. Finally, the results of cycle life experiment under alternating temperature condition show that the maximum estimation error of the proposed adaptive estimation scheme is less than 2%.

Key words: capacity estimation, cycle life, Arrhenius model, model parameters, incremental PID