• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2022, Vol. 58 ›› Issue (17): 96-104.doi: 10.3901/JME.2022.17.096

• 特邀专栏:先进机电装备可靠性与智能化 • 上一篇    下一篇

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基于递推最小二乘法的锂电池内短路全寿命周期辨识

何晋, 马睿飞, 蔡琦琳, 范学良, 赵威风, 邓业林   

  1. 苏州大学轨道交通学院 苏州 215131
  • 收稿日期:2021-07-19 修回日期:2022-01-05 发布日期:2022-11-07
  • 作者简介:何晋,男,1998年生,硕士研究生。主要研究方向为电池热管理,模型在线辨识等。E-mail:jhe0129@stu.suda.edu.cn

Life Cycle Identification of Internal Short Circuits of Lithium-ion Battery Based on Recursive Least Square Method

HE Jin, MA Ruifei, CAI Qilin, FAN Xueliang, ZHAO Weifeng, DENG Yelin   

  1. School of Rail Transportation, Soochow University, Suzhou 215131
  • Received:2021-07-19 Revised:2022-01-05 Published:2022-11-07
  • Contact: 国家自然科学基金(51905361)、中国博士后科学基金(2021M702391)和江苏省博士后科学基金(2021K358C)资助项目。

摘要: 锂电池内短路发展到后期阶段会引发热失控造成严重安全问题,因此必须在前中期识别内短路。当前检测内短路的常见方式是利用同一电池组内电芯间电压等参数的一致性,通过比较成组电芯间性能差异,筛选出异常电芯。然而对于退役电池等已经老化的电池组,其成组结构很可能已被打乱,且电芯间本已存在性能分化,无法使用该方法。为此,锂电池极化内阻被选作独立识别电芯内短路的标志性参数,而带遗忘因子的递推最小二乘法被用于在线辨识内短路前后极化内阻变化以在全寿命周期内识别内短路。针对电池老化影响辨识结果精度的问题,首先通过选择合适的遗忘因子与采样频率来优化该算法以适应老化对模型的影响。然后利用优化后的算法进行电池表面温度仿真模拟,进一步验证该算法的精度。最后设计内短路实验,验证该算法识别内短路的能力。结果表明,当遗忘因子为0.95,数据采样间隔为1 s时,该算法适应老化的能力最强,利用其进行电池表面温度估计的误差在2%以内。在内短路发展到后期阶段前,电池健康状态(SOH)为80%的电池极化内阻均值增加达到45%及以上,可以有效地识别内短路。

关键词: 内短路, 全寿命周期, 参数辨识, 最小二乘法, 温度估计

Abstract: Internal short circuits(ISCs) of Li-ion batteries in the later stages can lead to thermal runaway and cause serious safety problems, so it is important to identify ISCs in the early or middel stage. The common way to detect ISCs is to use the consistency of voltage and other parameters between cells in the same battery pack, and abnormal cells are screened out by comparing performance of cells within a battery pack. However, for battery packs that have been aged, such as retired batteries, the pack structure is likely to be disrupted. Moreover, there is already a performance differentiation between the cells. So This method is not suitable for ageing batteries. Therefore, the polarization resistance is chosen as an independent landmark parameter for identifying ISCs in the cells, while recursive least squares with a forgetting factor is used to identify polarization resistance online to identify ISCs in the whole life cycle. To address the problem that battery ageing affects the accuracy of the identification results, the algorithm is first optimised to suit the effects of ageing on the model by selecting an appropriate forgetting factor and sampling frequency. The optimised algorithm is then used to simulate the surface temperature of the cells to further verify the accuracy of the algorithm. Finally, an ISC experiment is designed to verify the ability of the algorithm to identify ISC. The results show that when the forgetting factor is 0.95 and the data sampling interval is 1s, the algorithm has the strongest ability to adapt to aging. And that the error in battery surface temperature estimation is within 2%. The mean increase in polarization resistance for state of health(SOH) of 80% reaches 45% and above before the ISC develops to a later stage, indicating that it can effectively identify ISC.

Key words: internal short circuit, life cycle, parameter identification, least square method, temperature prediction

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