An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery
An accurate state-of-charge (SOC) can not only provide a safe and reliable guarantee for the entirety of equipment but also extend the service life of the battery pack. Given that the chemical reaction inside the lithium-ion battery is a highly nonlinear dynamic system, obtaining an accurate SOC for...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/23/6366 |
| _version_ | 1797545889530642432 |
|---|---|
| author | Wenxian Duan Chuanxue Song Silun Peng Feng Xiao Yulong Shao Shixin Song |
| author_facet | Wenxian Duan Chuanxue Song Silun Peng Feng Xiao Yulong Shao Shixin Song |
| author_sort | Wenxian Duan |
| collection | DOAJ |
| description | An accurate state-of-charge (SOC) can not only provide a safe and reliable guarantee for the entirety of equipment but also extend the service life of the battery pack. Given that the chemical reaction inside the lithium-ion battery is a highly nonlinear dynamic system, obtaining an accurate SOC for the battery management system is very challenging. This paper proposed a gated recurrent unit recurrent neural network model with activation function layers (GRU-ATL) to estimate battery SOC. The model used deep learning technology to establish the nonlinear relationship between current, voltage, and temperature measurement signals and battery SOC. Then the online SOC estimation was carried out on different testing sets using the trained model. The experiments in this paper showed that the GRU-ATL network model could realize online SOC estimation under different working conditions without relying on an accurate battery model. Compared with the gated recurrent unit recurrent neural (GRU) network model and long short-term memory (LSTM) network model, the GRU-ATL network model had more stable and accurate SOC prediction performance. When the measurement data contained noise, the experimental results showed that the SOC prediction accuracy of GRU-ATL model was 0.1–0.4% higher than the GRU model and 0.3–0.7% higher than the LSTM model. The mean absolute error (MAE) of SOC predicted by the GRU-ATL model was stable in the range of 0.7–1.4%, and root mean square error (RMSE) was stable between 1.2–1.9%. The model still had high prediction accuracy and robustness, which could meet the SOC estimation in complex vehicle working conditions. |
| first_indexed | 2024-03-10T14:22:20Z |
| format | Article |
| id | doaj.art-61426518165349ad8574b4cfc5083108 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T14:22:20Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-61426518165349ad8574b4cfc50831082023-11-20T23:17:58ZengMDPI AGEnergies1996-10732020-12-011323636610.3390/en13236366An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion BatteryWenxian Duan0Chuanxue Song1Silun Peng2Feng Xiao3Yulong Shao4Shixin Song5State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, ChinaZhengzhou Yutong Bus Co., Ltd., Zhengzhou 450016, ChinaSchool of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, ChinaAn accurate state-of-charge (SOC) can not only provide a safe and reliable guarantee for the entirety of equipment but also extend the service life of the battery pack. Given that the chemical reaction inside the lithium-ion battery is a highly nonlinear dynamic system, obtaining an accurate SOC for the battery management system is very challenging. This paper proposed a gated recurrent unit recurrent neural network model with activation function layers (GRU-ATL) to estimate battery SOC. The model used deep learning technology to establish the nonlinear relationship between current, voltage, and temperature measurement signals and battery SOC. Then the online SOC estimation was carried out on different testing sets using the trained model. The experiments in this paper showed that the GRU-ATL network model could realize online SOC estimation under different working conditions without relying on an accurate battery model. Compared with the gated recurrent unit recurrent neural (GRU) network model and long short-term memory (LSTM) network model, the GRU-ATL network model had more stable and accurate SOC prediction performance. When the measurement data contained noise, the experimental results showed that the SOC prediction accuracy of GRU-ATL model was 0.1–0.4% higher than the GRU model and 0.3–0.7% higher than the LSTM model. The mean absolute error (MAE) of SOC predicted by the GRU-ATL model was stable in the range of 0.7–1.4%, and root mean square error (RMSE) was stable between 1.2–1.9%. The model still had high prediction accuracy and robustness, which could meet the SOC estimation in complex vehicle working conditions.https://www.mdpi.com/1996-1073/13/23/6366state-of-chargelithium-ion batterygated recurrent unitnon-Gaussian noisesrobustness |
| spellingShingle | Wenxian Duan Chuanxue Song Silun Peng Feng Xiao Yulong Shao Shixin Song An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery Energies state-of-charge lithium-ion battery gated recurrent unit non-Gaussian noises robustness |
| title | An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery |
| title_full | An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery |
| title_fullStr | An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery |
| title_full_unstemmed | An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery |
| title_short | An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery |
| title_sort | improved gated recurrent unit network model for state of charge estimation of lithium ion battery |
| topic | state-of-charge lithium-ion battery gated recurrent unit non-Gaussian noises robustness |
| url | https://www.mdpi.com/1996-1073/13/23/6366 |
| work_keys_str_mv | AT wenxianduan animprovedgatedrecurrentunitnetworkmodelforstateofchargeestimationoflithiumionbattery AT chuanxuesong animprovedgatedrecurrentunitnetworkmodelforstateofchargeestimationoflithiumionbattery AT silunpeng animprovedgatedrecurrentunitnetworkmodelforstateofchargeestimationoflithiumionbattery AT fengxiao animprovedgatedrecurrentunitnetworkmodelforstateofchargeestimationoflithiumionbattery AT yulongshao animprovedgatedrecurrentunitnetworkmodelforstateofchargeestimationoflithiumionbattery AT shixinsong animprovedgatedrecurrentunitnetworkmodelforstateofchargeestimationoflithiumionbattery AT wenxianduan improvedgatedrecurrentunitnetworkmodelforstateofchargeestimationoflithiumionbattery AT chuanxuesong improvedgatedrecurrentunitnetworkmodelforstateofchargeestimationoflithiumionbattery AT silunpeng improvedgatedrecurrentunitnetworkmodelforstateofchargeestimationoflithiumionbattery AT fengxiao improvedgatedrecurrentunitnetworkmodelforstateofchargeestimationoflithiumionbattery AT yulongshao improvedgatedrecurrentunitnetworkmodelforstateofchargeestimationoflithiumionbattery AT shixinsong improvedgatedrecurrentunitnetworkmodelforstateofchargeestimationoflithiumionbattery |