SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage rise

SUN Ning; WANG Shenghui; YU Tianjian; DAI Yi

doi:10.13890/j.issn.1000-128X.2022.05.015

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SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage rise

Electric Traction and Control | 更新时间：2024-08-02

- SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage rise
- Electric drive for locomotives Issue 5, Pages: 103-108(2022)
- 作者机构：
  
  1.中车青岛四方机车车辆股份有限公司，山东青岛 266111
  2.中南大学交通运输工程学院，湖南长沙;410075
- 作者简介：
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2022.05.015
  CLC： TM912
- Published：10 September 2022，
  
  Received：15 July 2022，
- 稿件说明：
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SUN Ning, WANG Shenghui, YU Tianjian, et al. SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage rise. [J]. Electric drive for locomotives (5):103-108(2022)
DOI：

SUN Ning, WANG Shenghui, YU Tianjian, et al. SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage rise. [J]. Electric drive for locomotives (5):103-108(2022) DOI： 10.13890/j.issn.1000-128X.2022.05.015.

摘要

动车组镍镉蓄电池健康状态会影响列车运行安全，但由于动车组运行工况复杂，导致现有方法无法较好地在线监测电池健康状态。为研究蓄电池健康状态变化趋势和实现在线预测，假设蓄电池在通过充电机充电的过程中无放电过程。为此，提出基于等压升充电时间的蓄电池健康状态在线预测方法，该方法通过对基于移动电压窗口的等压升充电时间与蓄电池健康状态综合相关性分析以确定最佳的等压升电压区间（即充电起始电压与截止电压之间），再通过等压升电压区间提取最佳等压升充电时间作为长短期记忆网络模型输入，利用麻雀搜索算法对长短期记忆网络参数寻优，建立蓄电池健康状态预测模型，实现了蓄电池健康状态的在线预测。试验结果表明，相较于传统长短期记忆网络和反向传播神经网络，基于麻雀搜索算法优化长短期记忆网络的蓄电池健康状态预测模型具有更高的预测精度。

Abstract

The state of health (SOH) of nickel-cadmium battery used on EMUs influences the operation safety of trains. Due to the complex working conditions of EMUs

the existing SOH monitoring methods are unable to realize good online monitoring. To study the change tendency of battery SOH and realize online prediction

this paper proposed an online SOH prediction method based on the charging time under constant voltage rise

with the assumption that there is no discharge when using charger to charge the battery. This method determined the optimal constant voltage rising range (i.e. the voltage difference between the start and end of charging) through a comprehensive correlation analysis of charging time under constant voltage rise and battery SOH based on moving voltage window

and then extracted the optimal charging time under constant voltage rise from the voltage range as the input for the long short-term memory (LSTM) model. The sparrow search algorithm (SSA) was used to optimize the LSTM parameters

establishing a battery SOH prediction model

and realizing the online prediction of battery SOH. The test results show that

compared with traditional LSTM and back propagation (BP) neural network

the battery SOH prediction model based on SSA-LSTM has higher prediction accuracy.

关键词

动车组镍镉蓄电池SOH预测等压升充电时间SSA-LSTM

Keywords

EMUsnickel-cadmium batterySOH predictioncharging time under constant voltage riseSSA-LSTM

references

ZHANG Liwei, YUN Lansi, SUN Minghe, et al. Simulation research on auxiliary power supply system of China standard EMU[J]. Electronics, 2019, 8(6): 647.

李奇, 刘嘉蔚, 陈维荣. 质子交换膜燃料电池剩余使用寿命预测方法综述及展望[J]. 中国电机工程学报, 2019, 39(8): 2365-2375.

LI Qi, LIU Jiawei, CHEN Weirong. Review and prospect of remaining useful life prediction methods for proton exchange membrane fuel cell[J]. Proceedings of the CSEE, 2019, 39(8): 2365-2375.

DE LARA J, GUERRA E. A posteriori typing for model-driven engineering: concepts, analysis, and applications[C]//ACM. Proceedings of the 40th International Conference on Software Engineering. Gothenburg: Association for Computing Machinery, 2018: 1136.

LIU Qianqian, ZHANG Jingyuan, LI Ke, et al. The remaining useful life prediction by using electrochemical model in the particle filter framework for lithium-ion batteries[J]. IEEE Access, 2020, 8: 126661-126670.

王萌. 干式荷电铅酸蓄电池健康状态检测与实现[D]. 石家庄: 河北科技大学, 2020.

WANG Meng. Detection and realization of health status of dry-type charged lead-acid batteries[D]. Shijiazhuang: Hebei University of Science and Technology, 2020.

张利国, 李宇剑, 刘乐, 等. 基于参数分析的蓄电池模型健康状态估计[J]. 自动化仪表, 2022, 43(2): 69-75.

ZHANG Liguo, LI Yujian, LIU Le, et al. Health state estimation of battery model based on parameter analysis[J]. Process Automation Instrumentation, 2022, 43(2): 69-75.

VARINI M, CAMPANA P E, LINDBERGH G. A semi-empirical, electrochemistry-based model for Li-ion battery performance prediction over lifetime[J]. Journal of Energy Storage, 2019, 25: 100819.

YAN Wuzhao, ZHANG Bin, ZHAO Guangquan, et al. A battery management system with a lebesgue-sampling-based extended Kalman filter[J]. IEEE Transactions on Industrial Electronics, 2018, 66(4): 3227-3236.

MEJDOUBI A E, CHAOUI H, GUALOUS H, et al. Lithium-ion batteries health prognosis considering aging conditions[J]. IEEE Transactions on Power Electronics, 2019, 34(7): 6834-6844.

TSENG K H, LIANG Jinwei, CHANG W, et al. Regression models using fully discharged voltage and internal resistance for state of health estimation of lithium-ion batteries[J]. Energies, 2015, 8(4): 2889-2907.

KHUMPROM P, YODO N. Data-driven prognostic model of Li-ion battery with deep learning algorithm[C]//IEEE. 2019 Annual Reliability and Maintainability Symposium (RAMS). Orlando: IEEE, 2019: 1-6.

CHEHADE A A, HUSSEIN A A. A collaborative gaussian process regression model for transfer learning of capacity trends between Li-ion battery cells[J]. IEEE Transactions on Vehicular Technology, 2020, 69(9): 9542-9552.

LIN Mingqiang, ZENG Xianping, WU Ji. State of health estimation of lithium-ion battery based on an adaptive tunable hybrid radial basis function network[J]. Journal of Power Sources, 2021, 504: 230063.

LEE J, LEE I. Online estimation algorithm of SOC and SOH using neural network for lithium battery[C]//IEEE. 2021 IEEE 3rd Eurasia Conference on IOT, Communication and Engineering (ECICE). Yunlin: IEEE, 2021: 568-571.

郭玄, 朱凯. 基于SSA-LSTM的锂离子电池寿命预测[J]. 电池工业, 2021, 25(3): 131-135.

GUO Xuan, ZHU Kai. Life prediction of lithium-ion battery based on SSA-LSTM[J]. Chinese Battery Industry, 2021, 25(3): 131-135.

薛建凯. 一种新型的群智能优化技术的研究与应用——麻雀搜索算法[D]. 上海: 东华大学, 2020.

XUE Jiankai. Research and application of a novel swarm intelligence optimization technique-sparrow search algorithm[D]. Shanghai: Donghua University, 2020.

赵婧宇, 池越, 周亚同. 基于SSA-LSTM模型的短期电力负荷预测[J]. 电工电能新技术, 2022, 41(6): 71-79.

ZHAO Jingyu, CHI Yue, ZHOU Yatong. Short-term load forecasting based on SSA-LSTM model[J]. Advanced Technology of Electrical Engineering and Energy, 2022, 41(6): 71-79.

孙全, 孙渊. 基于麻雀搜索算法的BP神经网络优化技术[J]. 上海电机学院学报, 2022, 25(1): 12-16.

SUN Quan, SUN Yuan. Optimization technology of BP neural network based on sparrow search algorithm[J]. Journal of Shanghai Dianji University, 2022, 25(1): 12-16.

WEI Meng, YE Min, LI Jiabo, et al. State of charge estimation of lithium-ion batteries using LSTM and NARX neural networks[J]. IEEE Access, 2020, 8: 189236-189245.

成庶, 甘沁洁, 赵明, 等. 镉镍蓄电池寿命预测的PF-LSTM建模方法研究[J]. 铁道科学与工程学报, 2020, 17(7): 1825-1832.

CHENG Shu, GAN Qinjie, ZHAO Ming, et al. PF-LSTM modeling method for life prediction of Ni-Cd battery[J]. Journal of Railway Science and Engineering, 2020, 17(7): 1825-1832.

ASSEFI M, HOOSHMAND A, HOSSEINI H, et al. Battery degradation temporal modeling using LSTM networks[C]//IEEE. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Orlando: IEEE, 2018: 853-858.

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