SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage rise
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Drive for Locomotives
2022-09-01
|
| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2022.05.015 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849729355525128192 |
|---|---|
| author | SUN Ning WANG Shenghui YU Tianjian DAI Yi |
| author_facet | SUN Ning WANG Shenghui YU Tianjian DAI Yi |
| author_sort | SUN Ning |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e85d11750fa249cda5fef15fb44dca3c |
| institution | DOAJ |
| issn | 1000-128X |
| language | zho |
| publishDate | 2022-09-01 |
| publisher | Editorial Department of Electric Drive for Locomotives |
| record_format | Article |
| series | 机车电传动 |
| spelling | doaj-art-e85d11750fa249cda5fef15fb44dca3c2025-08-20T03:09:15ZzhoEditorial Department of Electric Drive for Locomotives机车电传动1000-128X2022-09-0110310832276278SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage riseSUN NingWANG ShenghuiYU TianjianDAI YiThe 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.http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2022.05.015EMUsnickel-cadmium batterySOH predictioncharging time under constant voltage riseSSA-LSTM |
| spellingShingle | SUN Ning WANG Shenghui YU Tianjian DAI Yi SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage rise 机车电传动 EMUs nickel-cadmium battery SOH prediction charging time under constant voltage rise SSA-LSTM |
| title | SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage rise |
| title_full | SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage rise |
| title_fullStr | SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage rise |
| title_full_unstemmed | SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage rise |
| title_short | SOH Prediction of nickel-cadmium battery based on the charging time under constant voltage rise |
| title_sort | soh prediction of nickel cadmium battery based on the charging time under constant voltage rise |
| topic | EMUs nickel-cadmium battery SOH prediction charging time under constant voltage rise SSA-LSTM |
| url | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2022.05.015 |
| work_keys_str_mv | AT sunning sohpredictionofnickelcadmiumbatterybasedonthechargingtimeunderconstantvoltagerise AT wangshenghui sohpredictionofnickelcadmiumbatterybasedonthechargingtimeunderconstantvoltagerise AT yutianjian sohpredictionofnickelcadmiumbatterybasedonthechargingtimeunderconstantvoltagerise AT daiyi sohpredictionofnickelcadmiumbatterybasedonthechargingtimeunderconstantvoltagerise |