Efficient Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Using Classifier Fusion Technique
<p dir="ltr">This paper presents an efficient energy management strategy for Fuel Cell Hybrid Electric Vehicles (FCHEV) using a Machine Learning (ML) approach. Petroleum-based fuels are utilised in conventional cars to provide good performance and long-distance speed. There are certa...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , |
| منشور في: |
2023
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513527416356864 |
|---|---|
| author | Debasis Chatterjee (17983789) |
| author2 | Pabitra Kumar Biswas (12757316) Chiranjit Sain (12507415) Amarjit Roy (17983792) Furkan Ahmad (709809) |
| author2_role | author author author author |
| author_facet | Debasis Chatterjee (17983789) Pabitra Kumar Biswas (12757316) Chiranjit Sain (12507415) Amarjit Roy (17983792) Furkan Ahmad (709809) |
| author_role | author |
| dc.creator.none.fl_str_mv | Debasis Chatterjee (17983789) Pabitra Kumar Biswas (12757316) Chiranjit Sain (12507415) Amarjit Roy (17983792) Furkan Ahmad (709809) |
| dc.date.none.fl_str_mv | 2023-09-06T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3312618 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Efficient_Energy_Management_Strategy_for_Fuel_Cell_Hybrid_Electric_Vehicles_Using_Classifier_Fusion_Technique/25239505 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Energy management Fuel cells Data models Support vector machines Optimization Fuels Integrated circuit modeling Nearest neighbor methods Electric vehicles Nanostructured materials Energy storage K-nearest neighbor (KNN) fuel cell hybrid electric vehicle (FCHEV) model predictive control (MPC) nanostructures for electrical energy storage (NEES) |
| dc.title.none.fl_str_mv | Efficient Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Using Classifier Fusion Technique |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This paper presents an efficient energy management strategy for Fuel Cell Hybrid Electric Vehicles (FCHEV) using a Machine Learning (ML) approach. Petroleum-based fuels are utilised in conventional cars to provide good performance and long-distance speed. There are certain disadvantages to using petrol or diesel, such as poor fuel economy and pollution-causing exhaust gas emissions. Furthermore, there are some limitations with existing available work, and the merger of these different optimisation techniques will be advantageous for achieving optimal performance. To address them, the purpose of this research is to create an efficient energy management approach by combining SVM, KNN, and the Naive Bayes technique. Additionally, by combining these classifier techniques better performing EMS is developed. Using the proposed features, the optimisation approach’s performance accuracy is increased. Furthermore, these individual classifiers comprising of SVM, KNN & Naïve Bayes is giving accuracy percentage of 96%, 92% & 94% respectively. Finally, after combining these three classifiers we have achieved an accuracy percentage of 98%.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3312618" target="_blank">https://dx.doi.org/10.1109/access.2023.3312618</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_5a50e8ea0da7352b44acd2490c32f324 |
| identifier_str_mv | 10.1109/access.2023.3312618 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25239505 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Efficient Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Using Classifier Fusion TechniqueDebasis Chatterjee (17983789)Pabitra Kumar Biswas (12757316)Chiranjit Sain (12507415)Amarjit Roy (17983792)Furkan Ahmad (709809)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringEnergy managementFuel cellsData modelsSupport vector machinesOptimizationFuelsIntegrated circuit modelingNearest neighbor methodsElectric vehiclesNanostructured materialsEnergy storageK-nearest neighbor (KNN)fuel cell hybrid electric vehicle (FCHEV)model predictive control (MPC)nanostructures for electrical energy storage (NEES)<p dir="ltr">This paper presents an efficient energy management strategy for Fuel Cell Hybrid Electric Vehicles (FCHEV) using a Machine Learning (ML) approach. Petroleum-based fuels are utilised in conventional cars to provide good performance and long-distance speed. There are certain disadvantages to using petrol or diesel, such as poor fuel economy and pollution-causing exhaust gas emissions. Furthermore, there are some limitations with existing available work, and the merger of these different optimisation techniques will be advantageous for achieving optimal performance. To address them, the purpose of this research is to create an efficient energy management approach by combining SVM, KNN, and the Naive Bayes technique. Additionally, by combining these classifier techniques better performing EMS is developed. Using the proposed features, the optimisation approach’s performance accuracy is increased. Furthermore, these individual classifiers comprising of SVM, KNN & Naïve Bayes is giving accuracy percentage of 96%, 92% & 94% respectively. Finally, after combining these three classifiers we have achieved an accuracy percentage of 98%.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3312618" target="_blank">https://dx.doi.org/10.1109/access.2023.3312618</a></p>2023-09-06T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3312618https://figshare.com/articles/journal_contribution/Efficient_Energy_Management_Strategy_for_Fuel_Cell_Hybrid_Electric_Vehicles_Using_Classifier_Fusion_Technique/25239505CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252395052023-09-06T06:00:00Z |
| spellingShingle | Efficient Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Using Classifier Fusion Technique Debasis Chatterjee (17983789) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Energy management Fuel cells Data models Support vector machines Optimization Fuels Integrated circuit modeling Nearest neighbor methods Electric vehicles Nanostructured materials Energy storage K-nearest neighbor (KNN) fuel cell hybrid electric vehicle (FCHEV) model predictive control (MPC) nanostructures for electrical energy storage (NEES) |
| status_str | publishedVersion |
| title | Efficient Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Using Classifier Fusion Technique |
| title_full | Efficient Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Using Classifier Fusion Technique |
| title_fullStr | Efficient Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Using Classifier Fusion Technique |
| title_full_unstemmed | Efficient Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Using Classifier Fusion Technique |
| title_short | Efficient Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Using Classifier Fusion Technique |
| title_sort | Efficient Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Using Classifier Fusion Technique |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Energy management Fuel cells Data models Support vector machines Optimization Fuels Integrated circuit modeling Nearest neighbor methods Electric vehicles Nanostructured materials Energy storage K-nearest neighbor (KNN) fuel cell hybrid electric vehicle (FCHEV) model predictive control (MPC) nanostructures for electrical energy storage (NEES) |