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| Lithium battery health evaluation based on multiscale data fusion |
| 1. School of Power Engineering, Naval University of Engineering, Wuhan， Hubei 430033, China； 2. School of Mechanical and Electrical Engineering, Wuhan City Polytechnic, Wuhan， Hubei 430064, China |
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Abstract A method of lithium battery health assessment was proposed based on the framework of multiscale data fusion with artificial neural network as the core. The internal constant resistance, the sample entropy of charging voltage and the isobaric discharge time were selected as typical characteristic parameters. The threelayer distributed artificial neural network was established for the multiscale data fusion, and the calculated fitting output was used as reference value for the health assessment. The proposed method was verified by the national aeronautics and space administration（NASA） experimental datasheet. The results show that the method based on the lithium battery typical characteristic parameters and the multiscale data fusion framework can rapidly iteratively converge to complete the evaluation and fitting of the health status of lithium battery. Comparing the calculation results of the proposed method with the test platform data, the average error is less than 3%, and the evaluation performance degradation trend is consistent with the actual deterioration trend.
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| ［1］ |
郝刚,金涛,于利峰.基于SFMEA和三角模糊软集的电池系统风险评估[J].江苏大学学报(自然科学版),2019,40(6):629-635.
|
|
HAO G, JIN T, YU L F. Risk evaluation in Liion battery system based on SFMEA and triangular fuzzy soft sets[J]. Journal of Jiangsu University（Natural Science Edition）, 2019,40(6):629-635.（in Chinese）
|
| ［2］ |
LI Y, LIU K L, FOLEY A M, et al. Datadriven health estimation and lifetime prediction of lithiumion batteries: a review[J]. Renewable and Sustainable Energy Reviews, DOI: 10.1016/j.rser.2019.109254.
|
| ［3］ |
陈猛,乌江,焦朝勇, 等.锂离子电池健康状态多因子在线估计方法[J].西安交通大学学报,2020,54(1):169-175.
|
|
CHEN M, WU J, JIAO C Y, et al. Multifactor online estimation method for health status of lithiumion battery[J]. Journal of Xi′an Jiaotong University, 2020,54(1):169-175.（in Chinese）
|
| ［4］ |
MENG H X, LI Y F. A review on prognostics and health management(PHM) methods of lithiumion batteries[J]. Renewable and Sustainable Energy Reviews,DOI: 10.1016/j.rser.2019.109405.
|
| ［5］ |
ZHANG S S, XU K, JOW T R. The low temperature performance of Liion batteries[J].Journal of Power Sources, 2003，115:137-140.
|
| ［6］ |
RICHMAN J S，MOORMAN J R. Physiological timeseries analysis using approximate entropy and sample entropy[J]. American Journal of Physiology Heart and Circulatory Physiology,2000,278:2039-2049.
|
| ［7］ |
LAKE D E, RICHMAN J S, GRIFFIN M P, et al. Sample entropy analysis of neonatal heart rate variability[J]. American Journal of Physiology Regulatory, Integrative and Comparative Physiology,2002,283:789-797.
|
| ［8］ |
SAHA B，GOEBEL K. ″Battery Data Set″, NASA ames prognostics data repository[EB/OL]. [2021-08-19].http:∥ti.arc.nasa.gov/project/prognosticdatarepository.
|
| ［9］ |
WIDODO A, SHIM M C, CAESERENDRA W, et al. Intelligent prognostics for battery health monitoring based on sample entropy[J].Expert Systems with Applications,2011,38:11763-11769.
|
| [10] |
林娅. 基于数据驱动的锂电池剩余使用寿命预测方法研究[D].南京：南京航空航天大学,2018.
|
| [11] |
WULLIARD N, HE W, OSTERMAN M, et al. Comparative analysis of features for determining state of health in lithiumion batteries[J]. International Journal of Prognostics and Health Management,2013,4（1）:14-20.
|
|
|
|
