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基于等压升充电时间的镍镉蓄电池SOH预测
SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage rise
- 机车电传动 2022年第5期 页码：103-108
- 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：
王升晖（1987—），男，硕士，研究方向为控制理论与控制工程； E-mail: wangshenghui@cqsf.com
- 基金信息：湖南省自然科学基金项目(2020JJ5757)
DOI：10.13890/j.issn.1000-128X.2022.05.015
中图分类号： TM912纸质出版日期：2022-09-10，
收稿日期：2022-07-15，
- 稿件说明：
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孙宁, 王升晖, 于天剑, 等. 基于等压升充电时间的镍镉蓄电池SOH预测[J]. 机车电传动, 2022,(5):103-108.
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, 2022,(5):103-108.
孙宁, 王升晖, 于天剑, 等. 基于等压升充电时间的镍镉蓄电池SOH预测[J]. 机车电传动, 2022,(5):103-108. DOI： 10.13890/j.issn.1000-128X.2022.05.015.
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, 2022,(5):103-108. 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
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