Research on SOC evaluation method and simulation of lithiumbattery based on echo state network
Taking lithium battery of new energy vehicles as the research object,an echo state network (ESN) model is established to predict the state of charge (SOC) of the vehicle's lithium battery. The cross-validation method is used to optimize the parameters of the ESN to solve difficulty to select ar...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
National Computer System Engineering Research Institute of China
2023-01-01
|
| Series: | Dianzi Jishu Yingyong |
| Subjects: | |
| Online Access: | http://www.chinaaet.com/article/3000157972 |
| _version_ | 1797447738978205696 |
|---|---|
| author | Du Guangbo Cai Mao Zhang Xin Fan Xingming Cheng Jianghua |
| author_facet | Du Guangbo Cai Mao Zhang Xin Fan Xingming Cheng Jianghua |
| author_sort | Du Guangbo |
| collection | DOAJ |
| description | Taking lithium battery of new energy vehicles as the research object,an echo state network (ESN) model is established to predict the state of charge (SOC) of the vehicle's lithium battery. The cross-validation method is used to optimize the parameters of the ESN to solve difficulty to select arameters of the model. The echo state network is trained by recursive least squares method with forgetting factors to calculate the output weight matrix so as to improve the adaptability and accuracy of the network.The feasibility of the prediction algorithm is further analyzed and verified by the model simulation. The research further analyzes and compares the predicted SOC of the established ESN model, the BP neural network algorithm and radial basis function (RBF) network algorithm under UDDS, US06 and NYCC. The research results show that the established echo state network model is superior to the BP algorithm and RBF algorithm in estimating the performance and effect of lithium-ion battery SOC evaluation. Using ESN model to predict SOC has a good application prospect and can provide a reference for long-term and effective SOC prediction of the lithium battery. |
| first_indexed | 2024-03-09T14:00:17Z |
| format | Article |
| id | doaj.art-c01a8f37310d430a87980c06b058e3dd |
| institution | Directory Open Access Journal |
| issn | 0258-7998 |
| language | zho |
| last_indexed | 2024-03-09T14:00:17Z |
| publishDate | 2023-01-01 |
| publisher | National Computer System Engineering Research Institute of China |
| record_format | Article |
| series | Dianzi Jishu Yingyong |
| spelling | doaj.art-c01a8f37310d430a87980c06b058e3dd2023-11-30T09:14:31ZzhoNational Computer System Engineering Research Institute of ChinaDianzi Jishu Yingyong0258-79982023-01-01491455110.16157/j.issn.0258-7998.2230573000157972Research on SOC evaluation method and simulation of lithiumbattery based on echo state networkDu Guangbo0Cai Mao1Zhang Xin2Fan Xingming3Cheng Jianghua4China United Engineering Corporation Limited， Hangzhou 310052， ChinaDep.of Electrical Engineering & Automation， Guilin University of Electronic and Technology ，Guilin 541004， ChinaDep.of Electrical Engineering & Automation， Guilin University of Electronic and Technology ，Guilin 541004， ChinaDep.of Electrical Engineering & Automation， Guilin University of Electronic and Technology ，Guilin 541004， ChinaChina United Engineering Corporation Limited， Hangzhou 310052， ChinaTaking lithium battery of new energy vehicles as the research object,an echo state network (ESN) model is established to predict the state of charge (SOC) of the vehicle's lithium battery. The cross-validation method is used to optimize the parameters of the ESN to solve difficulty to select arameters of the model. The echo state network is trained by recursive least squares method with forgetting factors to calculate the output weight matrix so as to improve the adaptability and accuracy of the network.The feasibility of the prediction algorithm is further analyzed and verified by the model simulation. The research further analyzes and compares the predicted SOC of the established ESN model, the BP neural network algorithm and radial basis function (RBF) network algorithm under UDDS, US06 and NYCC. The research results show that the established echo state network model is superior to the BP algorithm and RBF algorithm in estimating the performance and effect of lithium-ion battery SOC evaluation. Using ESN model to predict SOC has a good application prospect and can provide a reference for long-term and effective SOC prediction of the lithium battery.http://www.chinaaet.com/article/3000157972 lithium batterystate of chargeecho state networkparameters optimization and selectioncross validation |
| spellingShingle | Du Guangbo Cai Mao Zhang Xin Fan Xingming Cheng Jianghua Research on SOC evaluation method and simulation of lithiumbattery based on echo state network Dianzi Jishu Yingyong lithium battery state of charge echo state network parameters optimization and selection cross validation |
| title | Research on SOC evaluation method and simulation of lithiumbattery based on echo state network |
| title_full | Research on SOC evaluation method and simulation of lithiumbattery based on echo state network |
| title_fullStr | Research on SOC evaluation method and simulation of lithiumbattery based on echo state network |
| title_full_unstemmed | Research on SOC evaluation method and simulation of lithiumbattery based on echo state network |
| title_short | Research on SOC evaluation method and simulation of lithiumbattery based on echo state network |
| title_sort | research on soc evaluation method and simulation of lithiumbattery based on echo state network |
| topic | lithium battery state of charge echo state network parameters optimization and selection cross validation |
| url | http://www.chinaaet.com/article/3000157972 |
| work_keys_str_mv | AT duguangbo researchonsocevaluationmethodandsimulationoflithiumbatterybasedonechostatenetwork AT caimao researchonsocevaluationmethodandsimulationoflithiumbatterybasedonechostatenetwork AT zhangxin researchonsocevaluationmethodandsimulationoflithiumbatterybasedonechostatenetwork AT fanxingming researchonsocevaluationmethodandsimulationoflithiumbatterybasedonechostatenetwork AT chengjianghua researchonsocevaluationmethodandsimulationoflithiumbatterybasedonechostatenetwork |