A Novel Energy Consumption Prediction Model Integrating Real-Time Traffic State Recognition and Velocity Prediction of BEVs
The widespread adoption of battery electric vehicles (BEVs) has highlighted the critical importance of precise energy consumption prediction models to address the problem of range anxiety among drivers. This study aims to enhance the accuracy of such models by combining real-time traffic state recog...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10772111/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841533457763663872 |
|---|---|
| author | Yue Li Yu Jiang Jianhua Guo Dong Xie |
| author_facet | Yue Li Yu Jiang Jianhua Guo Dong Xie |
| author_sort | Yue Li |
| collection | DOAJ |
| description | The widespread adoption of battery electric vehicles (BEVs) has highlighted the critical importance of precise energy consumption prediction models to address the problem of range anxiety among drivers. This study aims to enhance the accuracy of such models by combining real-time traffic state recognition and velocity prediction, thereby mitigating range anxiety and enhancing the driving experience. Consequently, we propose an improved Fuzzy C-Means (FCM) clustering algorithm that use historical traffic data and dynamic traffic information accurately identify traffic conditions. In addition, a Fuzzy-Markov-based velocity prediction model is developed to generate future velocity profiles under diverse traffic scenarios. In the energy consumption prediction stage, a particle swarm optimization-radial basis function neural network (PSO-RBFNN) model is employed to estimation the energy consumption. Simulation results show a significant improvement in prediction accuracy, with the Mean Absolute Percentage Error (MAPE) reduced to below 3.2% under diverse traffic scenarios. |
| format | Article |
| id | doaj-art-fe59eb6079dc43da8234f59c1f7d2b3b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fe59eb6079dc43da8234f59c1f7d2b3b2025-01-16T00:01:22ZengIEEEIEEE Access2169-35362024-01-011219617819619410.1109/ACCESS.2024.350945610772111A Novel Energy Consumption Prediction Model Integrating Real-Time Traffic State Recognition and Velocity Prediction of BEVsYue Li0https://orcid.org/0009-0006-4699-5471Yu Jiang1Jianhua Guo2https://orcid.org/0000-0002-9551-0112Dong Xie3College of Automotive Engineering, Jilin University, Changchun, ChinaCollege of Automotive Engineering, Jilin University, Changchun, ChinaCollege of Automotive Engineering, Jilin University, Changchun, ChinaCollege of Automotive Engineering, Jilin University, Changchun, ChinaThe widespread adoption of battery electric vehicles (BEVs) has highlighted the critical importance of precise energy consumption prediction models to address the problem of range anxiety among drivers. This study aims to enhance the accuracy of such models by combining real-time traffic state recognition and velocity prediction, thereby mitigating range anxiety and enhancing the driving experience. Consequently, we propose an improved Fuzzy C-Means (FCM) clustering algorithm that use historical traffic data and dynamic traffic information accurately identify traffic conditions. In addition, a Fuzzy-Markov-based velocity prediction model is developed to generate future velocity profiles under diverse traffic scenarios. In the energy consumption prediction stage, a particle swarm optimization-radial basis function neural network (PSO-RBFNN) model is employed to estimation the energy consumption. Simulation results show a significant improvement in prediction accuracy, with the Mean Absolute Percentage Error (MAPE) reduced to below 3.2% under diverse traffic scenarios.https://ieeexplore.ieee.org/document/10772111/Battery electric vehiclesenergy consumption predictiontraffic state recognitionvelocity prediction |
| spellingShingle | Yue Li Yu Jiang Jianhua Guo Dong Xie A Novel Energy Consumption Prediction Model Integrating Real-Time Traffic State Recognition and Velocity Prediction of BEVs IEEE Access Battery electric vehicles energy consumption prediction traffic state recognition velocity prediction |
| title | A Novel Energy Consumption Prediction Model Integrating Real-Time Traffic State Recognition and Velocity Prediction of BEVs |
| title_full | A Novel Energy Consumption Prediction Model Integrating Real-Time Traffic State Recognition and Velocity Prediction of BEVs |
| title_fullStr | A Novel Energy Consumption Prediction Model Integrating Real-Time Traffic State Recognition and Velocity Prediction of BEVs |
| title_full_unstemmed | A Novel Energy Consumption Prediction Model Integrating Real-Time Traffic State Recognition and Velocity Prediction of BEVs |
| title_short | A Novel Energy Consumption Prediction Model Integrating Real-Time Traffic State Recognition and Velocity Prediction of BEVs |
| title_sort | novel energy consumption prediction model integrating real time traffic state recognition and velocity prediction of bevs |
| topic | Battery electric vehicles energy consumption prediction traffic state recognition velocity prediction |
| url | https://ieeexplore.ieee.org/document/10772111/ |
| work_keys_str_mv | AT yueli anovelenergyconsumptionpredictionmodelintegratingrealtimetrafficstaterecognitionandvelocitypredictionofbevs AT yujiang anovelenergyconsumptionpredictionmodelintegratingrealtimetrafficstaterecognitionandvelocitypredictionofbevs AT jianhuaguo anovelenergyconsumptionpredictionmodelintegratingrealtimetrafficstaterecognitionandvelocitypredictionofbevs AT dongxie anovelenergyconsumptionpredictionmodelintegratingrealtimetrafficstaterecognitionandvelocitypredictionofbevs AT yueli novelenergyconsumptionpredictionmodelintegratingrealtimetrafficstaterecognitionandvelocitypredictionofbevs AT yujiang novelenergyconsumptionpredictionmodelintegratingrealtimetrafficstaterecognitionandvelocitypredictionofbevs AT jianhuaguo novelenergyconsumptionpredictionmodelintegratingrealtimetrafficstaterecognitionandvelocitypredictionofbevs AT dongxie novelenergyconsumptionpredictionmodelintegratingrealtimetrafficstaterecognitionandvelocitypredictionofbevs |