电气工程学报 ›› 2022, Vol. 17 ›› Issue (3): 58-65.doi: 10.11985/2022.03.007

• 特邀专栏:储能(储氢)材料、技术、装置及新能源综合应用 • 上一篇    下一篇

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基于温度变化率曲线的锂离子电池健康状态评估算法*

党月懋(), 张雪纯, 徐楚奕, 江全元()   

  1. 浙江大学电气工程学院 杭州 310027
  • 收稿日期:2022-06-28 修回日期:2022-08-15 出版日期:2022-09-25 发布日期:2022-10-28
  • 通讯作者: 江全元 E-mail:3190102289@zju.edu.cn;jqy@zju.edu.cn
  • 作者简介:党月懋,女,2000年生。主要研究方向为锂离子电池健康状态评估。E-mail: 3190102289@zju.edu.cn
  • 基金资助:
    *国家自然科学基金资助项目(51677164)

Lithium-ion Battery State of Health Assessment Algorithm Based on DT Curve

DANG Yuemao(), ZHANG Xuechun, XU Chuyi, JIANG Quanyuan()   

  1. College of Electrical Engineering, Zhejiang University, Hangzhou 310027
  • Received:2022-06-28 Revised:2022-08-15 Online:2022-09-25 Published:2022-10-28
  • Contact: JIANG Quanyuan E-mail:3190102289@zju.edu.cn;jqy@zju.edu.cn

摘要:

锂离子电池技术的日益成熟为新能源发电和电动汽车等产业发展提供了重要支撑作用。锂离子电池采用有机电解液,发生故障后极易触发电池材料的放热副反应,导致电池热失控,最终可能演化成燃烧爆炸等重大事故。电池健康状态(State of health,SOH)是锂离子电池储能系统故障诊断和安全预警的重要参数,精确估计SOH是提升电池系统安全性的有效方法。提出一种基于温度变化率(DT)曲线的锂离子电池健康状态评估算法,充分提取反映电池健康状态的锂离子电池表面温度信息,以电池充电过程中的DT曲线的极大值点和两极值间的电压差作为电池SOH估计的特征量,进而搭建了基于反向传播(Back propagation,BP)神经网络的SOH估计模型。结合试验数据和仿真,测试结果最终表明,所提出的方法可有效提升锂离子电池SOH的估计精度。

关键词: 锂离子电池, 电池管理系统, 温度, 电池健康状态, 神经网络

Abstract:

The increasing maturity of lithium-ion battery technology provides important support for the development of new energy power generation and electric vehicles. Lithium-ion battery adopts organic electrolyte, which is easy to trigger exothermic side reaction of battery material after failure, leading to thermal runaway of battery. And then it is likely to evolve into serious accidents such as combustion and explosion. State of health(SOH) is an important parameter for fault diagnosis and safety warning of lithium battery energy storage system. Accurate estimation of SOH is an effective way to improve system safety. Therefore, a temperature differential curve(DT curve) based lithium-ion battery health status assessment algorithm is proposed to fully extract the temperature information that is highly correlated with the battery health status on the surface of lithium-ion battery. The maximum point of DT curve and the voltage difference between the two extreme values in the battery charging process are taken as the characteristic quantity of SOH estimation. The SOH estimation model based on back propagation(BP) neural network is built. The test results of experiments and simulations finally show that the proposed method can effectively improve the SOH estimation accuracy of lithium-ion batteries.

Key words: Lithium-ion battery, battery management system(BMS), temperature, state of health, neural networks

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