An Improved Gated Recurrent Unit Neural Network for State-of-Charge Estimation of Lithium-Ion Battery
State-of-charge (SOC) estimation of lithium-ion battery is a key parameter of the battery management system (BMS). However, SOC cannot be obtained directly. In order to predict SOC accurately, we proposed a recurrent neural network called gated recurrent unit network that is based on genetic algorit...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/5/2305 |
| _version_ | 1797475761403199488 |
|---|---|
| author | Jianlong Chen Chenlei Lu Cong Chen Hangyu Cheng Dongji Xuan |
| author_facet | Jianlong Chen Chenlei Lu Cong Chen Hangyu Cheng Dongji Xuan |
| author_sort | Jianlong Chen |
| collection | DOAJ |
| description | State-of-charge (SOC) estimation of lithium-ion battery is a key parameter of the battery management system (BMS). However, SOC cannot be obtained directly. In order to predict SOC accurately, we proposed a recurrent neural network called gated recurrent unit network that is based on genetic algorithm (GA-GRU) in this paper. GA was introduced to optimize the key parameters of the model, which can improve the performance of the proposed network. Furthermore, batteries were tested under four dynamic driving conditions at five temperatures to establish training and testing datasets. Finally, the proposed method was validated on dynamic driving conditions and compared with other deep learning methods. The results show that the proposed method can achieve high accuracy and robustness. |
| first_indexed | 2024-03-09T20:48:25Z |
| format | Article |
| id | doaj.art-397a7808f52a4d83b54ce0d7ff5eea5e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:48:25Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-397a7808f52a4d83b54ce0d7ff5eea5e2023-11-23T22:38:41ZengMDPI AGApplied Sciences2076-34172022-02-01125230510.3390/app12052305An Improved Gated Recurrent Unit Neural Network for State-of-Charge Estimation of Lithium-Ion BatteryJianlong Chen0Chenlei Lu1Cong Chen2Hangyu Cheng3Dongji Xuan4College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaCollege of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaCollege of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaCollege of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaCollege of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaState-of-charge (SOC) estimation of lithium-ion battery is a key parameter of the battery management system (BMS). However, SOC cannot be obtained directly. In order to predict SOC accurately, we proposed a recurrent neural network called gated recurrent unit network that is based on genetic algorithm (GA-GRU) in this paper. GA was introduced to optimize the key parameters of the model, which can improve the performance of the proposed network. Furthermore, batteries were tested under four dynamic driving conditions at five temperatures to establish training and testing datasets. Finally, the proposed method was validated on dynamic driving conditions and compared with other deep learning methods. The results show that the proposed method can achieve high accuracy and robustness.https://www.mdpi.com/2076-3417/12/5/2305lithium-ion batterystate-of-charge estimationbattery management systemgenetic algorithmgated recurrent unit neural network |
| spellingShingle | Jianlong Chen Chenlei Lu Cong Chen Hangyu Cheng Dongji Xuan An Improved Gated Recurrent Unit Neural Network for State-of-Charge Estimation of Lithium-Ion Battery Applied Sciences lithium-ion battery state-of-charge estimation battery management system genetic algorithm gated recurrent unit neural network |
| title | An Improved Gated Recurrent Unit Neural Network for State-of-Charge Estimation of Lithium-Ion Battery |
| title_full | An Improved Gated Recurrent Unit Neural Network for State-of-Charge Estimation of Lithium-Ion Battery |
| title_fullStr | An Improved Gated Recurrent Unit Neural Network for State-of-Charge Estimation of Lithium-Ion Battery |
| title_full_unstemmed | An Improved Gated Recurrent Unit Neural Network for State-of-Charge Estimation of Lithium-Ion Battery |
| title_short | An Improved Gated Recurrent Unit Neural Network for State-of-Charge Estimation of Lithium-Ion Battery |
| title_sort | improved gated recurrent unit neural network for state of charge estimation of lithium ion battery |
| topic | lithium-ion battery state-of-charge estimation battery management system genetic algorithm gated recurrent unit neural network |
| url | https://www.mdpi.com/2076-3417/12/5/2305 |
| work_keys_str_mv | AT jianlongchen animprovedgatedrecurrentunitneuralnetworkforstateofchargeestimationoflithiumionbattery AT chenleilu animprovedgatedrecurrentunitneuralnetworkforstateofchargeestimationoflithiumionbattery AT congchen animprovedgatedrecurrentunitneuralnetworkforstateofchargeestimationoflithiumionbattery AT hangyucheng animprovedgatedrecurrentunitneuralnetworkforstateofchargeestimationoflithiumionbattery AT dongjixuan animprovedgatedrecurrentunitneuralnetworkforstateofchargeestimationoflithiumionbattery AT jianlongchen improvedgatedrecurrentunitneuralnetworkforstateofchargeestimationoflithiumionbattery AT chenleilu improvedgatedrecurrentunitneuralnetworkforstateofchargeestimationoflithiumionbattery AT congchen improvedgatedrecurrentunitneuralnetworkforstateofchargeestimationoflithiumionbattery AT hangyucheng improvedgatedrecurrentunitneuralnetworkforstateofchargeestimationoflithiumionbattery AT dongjixuan improvedgatedrecurrentunitneuralnetworkforstateofchargeestimationoflithiumionbattery |