Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method
文献类型：期刊论文
| 作者 | Zhang, Caiping2; Wang, Yubin2; Gao, Yang2; Wang, Fang1; Mu, Biqiang3; Zhang, Weige2 |
| 刊名 | APPLIED ENERGY
 |
| 出版日期 | 2019-12-15 |
| 卷号 | 256页码:10 |
| 关键词 | Nickel-Cobalt-Manganese lithium-ion battery Accelerated aging Sudden degradation Recognition Quantile regression |
| ISSN号 | 0306-2619 |
| DOI | 10.1016/j.apenergy.2019.113841 |
| 英文摘要 | The requirement for energy density of lithium-ion batteries becomes more urgent due to the rising demand for driving range of electric vehicles in recent years. Meanwhile, the performance stability of batteries with high energy densities tends to deteriorate, leading to accelerating degradation and safety issues. As a result, it is critical to explore the reasons that yield the sudden degradation and to recognize the degradation knee point of Nickel-Cobalt-Manganese batteries commonly used for electric vehicles. Existing results have disclosed that the lithium deposition of negative electrode dominates the sudden degradation of battery capacity. This paper extracts key parameters that characterize the aging status to facilitate knee point recognition in engineering practice. Furthermore, a novel method that integrates quantile regression and Monte Carlo simulation method to identify the accelerated fading knee point is introduced. The dynamic safety boundary determination method for the whole battery lifetime is proposed to update and monitor the safety zone. It is verified by experiments that the recognition results of capacity degradation knee point appear within 90-95% capacity range at 25 degrees C, 35 degrees C and 45 degrees C conditions, which can provide an early warning before the battery fails. Using the proposed method for recognizing the sudden degradation of capacity, recognition result is effective even if the input is disturbed and has strong reliability and stability under different conditions. It is helpful to promote the sustainable and stable development of the electric vehicles and improve advanced applied energy technologies. |
| WOS研究方向 | Energy & Fuels ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000497981300012 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/50347]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Wang, Yubin; Wang, Fang |
| 作者单位 | 1.China Automot Technol & Res Ctr Co Ltd CATARC, Tianjin Key Lab Evaluat Technol Elect Vehicles, Tianjin 300300, Peoples R China 2.Beijing Jiaotong Univ, Natl Act Distribut Network Technol Res Ctr NANTEC, Beijing 100044, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Caiping,Wang, Yubin,Gao, Yang,et al. Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method[J]. APPLIED ENERGY,2019,256:10. |
| APA | Zhang, Caiping,Wang, Yubin,Gao, Yang,Wang, Fang,Mu, Biqiang,&Zhang, Weige.(2019).Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method.APPLIED ENERGY,256,10. |
| MLA | Zhang, Caiping,et al."Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method".APPLIED ENERGY 256(2019):10. |
入库方式： OAI收割
来源：数学与系统科学研究院
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