期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 70, 期 7, 页码 6613-6625出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3087004
关键词
Degradation; Lithium-ion batteries; Predictive models; Estimation; Support vector machines; Recurrent neural networks; Prognostics and health management; Remaining charging-discharging cycles; lithium-ion batteries; Mann-Kendall analysis; equivalent degradation indicator; state-of-health; early-stage prediction
资金
- Science and Technology Development Fund, Macau SAR [0018/2019/AKP, 0008/2019/AGJ, SKL-IOTSC-2021-2023]
- Ministry of Science and Technology of China [2019YFB1600700]
- Guangdong Basic and Applied Basic Research Foundation [2020B151513000]
This study introduces the Mann-Kendall trend analysis to propose a new Equivalent Degradation Indicator (EDI) to replace the capacity-based State of Health (SOH) indicator, achieving accurate RCDC prediction and low computational complexity. Experimental results demonstrate the method's good early-stage prediction capability and high prognosis efficiency.
Online remaining charging-discharging cycle (RCDC) prognosis is of great significance for lithium-ion batteries. The conventional method is usually based on whether the state-of-health (SOH) of capacity reaches the end-of-life (EoL) threshold. However, the most available prediction methods have two problems that need to be solved. First, the SOH degradation curve of the lithium-ion battery is nonlinear and non-Gaussian, and the battery capacity regeneration phenomena (CRP) has a direct impact on RCDC estimation efficiency. These factors challenge the precise forecast of RCDC and increase the risk of prediction failure. Second, existing methods have insufficient early-stage prediction ability for capacity degradation because too little data are available to facilitate establishing and optimizing the prediction models. To overcome the above-mentioned drawbacks, this study introduces the Mann-Kendall trend analysis to generate an equivalent degradation indicator (EDI), and to replace the capacity-based SOH. The proposed EDI has good linearity and monotonicity, and is conducive to adopt a simple structured prediction model to determine the RCDC. Besides, this study is based on the SOH-EDI synchronization mapping relationship and applies an one-degree polynomial regression model to estimate the EoL threshold on the EDI curve. From the perspective of computational complexity, the proposed framework uses two polynomial prediction models with simple structures, which realizes a low computational burden and online RCDC prediction. To verify the efficiency of the proposed method, this paper introduces three methods for comparison. Experimental results show that the proposed framework has satisfied early-stage prediction ability of RCDC and has a superior prognosis efficiency.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据