| 雷 旭,张春玲,于明加,陈潇阳.退役电池快速检测分类方法研究[J].电子测量与仪器学报,2023,37(4):213-222 |
| 退役电池快速检测分类方法研究 |
| Research on fast screening and classification of retired batteries |
| |
| DOI： |
| 中文关键词: 退役电池 快速测试 聚类算法 充电曲线 |
| 英文关键词:retired battery fast screening clustering algorithm charging curve |
| 基金项目:陕西省重点研发计划重点产业创新链项目(2019ZDLGY15-04-02)、长安大学研究生科研创新实践(300103722005)项目资助 |
|
|
|
|
| 摘要点击次数: 1192 |
| 全文下载次数: 2945 |
| 中文摘要: |
| 随着大量锂离子电池从电动汽车上退役,对退役电池快速检测的研究迫在眉睫。 针对传统方法因初始状态差异,导致
电池在二次利用前的一致性检测时间较长问题,基于电池充电曲线提出了一种快速测试方法。 通过将电池充电至截止电压保
证电池具有相同的初始状态,而无需进行将电池放空以保证初始状态相同这一步骤,测试时间仅为电池完整充放电时间的
12. 5%,检测效率提高;提取特征后采用融合 Canopy 的 K-means++聚类算法在 NASA 数据集和实验室电池上进行验证,聚类准
确度达 80. 5%,证明了设计的快速测试方法的可行性。 |
| 英文摘要: |
| As a large number of lithium-ion batteries are being retired from electric vehicles, research on rapid screening of retired
batteries is urgent. Aiming at the problem that the consistency screening time of batteries before secondary utilization is long due to the
difference in initial state of traditional methods, this paper proposed a quick test method based on battery charging curve. By charging
the battery to the cut-off voltage to ensure that the battery has the same initial state, instead of emptying the battery, the test time is only
12. 5% of the complete battery charging and discharging time, and the screening efficiency was largely improved. After the features were
extracted, the K-means++ clustering algorithm combined with Canopy was used to verify the results on NASA data sets and laboratory
batteries. The clustering accuracy reached 80. 5%, which proved the feasibility of the designed rapid test method. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
