Development of an adaptive neuro‐fuzzy inference system–based equivalent consumption minimisation strategy to improve fuel economy in hybrid electric vehicles
Abstract The most viable option to achieve the goals of saving energy and protecting the environment is to replace conventional vehicles with hybrid electric vehicles (HEVs). In HEVs, the operational characteristics of an internal combustion engine (ICE) and an electric motor (EM) are different from...
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| Format: | Article |
| Language: | English |
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Wiley
2021-09-01
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| Series: | IET Electrical Systems in Transportation |
| Online Access: | https://doi.org/10.1049/els2.12020 |
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| _version_ | 1832546663811514368 |
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| author | Krishna Veer Singh Hari Om Bansal Dheerendra Singh |
| author_facet | Krishna Veer Singh Hari Om Bansal Dheerendra Singh |
| author_sort | Krishna Veer Singh |
| collection | DOAJ |
| description | Abstract The most viable option to achieve the goals of saving energy and protecting the environment is to replace conventional vehicles with hybrid electric vehicles (HEVs). In HEVs, the operational characteristics of an internal combustion engine (ICE) and an electric motor (EM) are different from each other and thus require an adaptive control strategy to achieve higher fuel economy along with smooth operation and better performance of the vehicle. An energy management control strategy is proposed for an HEV based on an adaptive network‐based fuzzy inference system (ANFIS). The proposed adaptive equivalent consumption minimisation strategy decides the power to be drawn from ICE and EM based on input parameters such as the speed of the vehicle, the state of charge of the battery, the EM torque and the ICE torque. The whole system is simulated in an advanced vehicle simulator tool. The proposed non‐linear controller has also been tested for real‐time behaviour using a field‐programmable gate array–based MicroLabBox hardware controller to compare its performance against existing controllers. The authors compared the fuel economy obtained using the proposed method with several other methods available in the literature. The comparison clearly reveals that the proposed ANFIS‐based method results in better optimization of energy and hence offers better fuel economy. The urban dynamometer driving schedule has been employed for this analysis. |
| format | Article |
| id | doaj-art-5b4c5bcec14f48fc8e3e584703c5a999 |
| institution | Kabale University |
| issn | 2042-9738 2042-9746 |
| language | English |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Electrical Systems in Transportation |
| spelling | doaj-art-5b4c5bcec14f48fc8e3e584703c5a9992025-02-03T06:47:35ZengWileyIET Electrical Systems in Transportation2042-97382042-97462021-09-0111317118510.1049/els2.12020Development of an adaptive neuro‐fuzzy inference system–based equivalent consumption minimisation strategy to improve fuel economy in hybrid electric vehiclesKrishna Veer Singh0Hari Om Bansal1Dheerendra Singh2Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Rajasthan IndiaDepartment of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Rajasthan IndiaDepartment of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Rajasthan IndiaAbstract The most viable option to achieve the goals of saving energy and protecting the environment is to replace conventional vehicles with hybrid electric vehicles (HEVs). In HEVs, the operational characteristics of an internal combustion engine (ICE) and an electric motor (EM) are different from each other and thus require an adaptive control strategy to achieve higher fuel economy along with smooth operation and better performance of the vehicle. An energy management control strategy is proposed for an HEV based on an adaptive network‐based fuzzy inference system (ANFIS). The proposed adaptive equivalent consumption minimisation strategy decides the power to be drawn from ICE and EM based on input parameters such as the speed of the vehicle, the state of charge of the battery, the EM torque and the ICE torque. The whole system is simulated in an advanced vehicle simulator tool. The proposed non‐linear controller has also been tested for real‐time behaviour using a field‐programmable gate array–based MicroLabBox hardware controller to compare its performance against existing controllers. The authors compared the fuel economy obtained using the proposed method with several other methods available in the literature. The comparison clearly reveals that the proposed ANFIS‐based method results in better optimization of energy and hence offers better fuel economy. The urban dynamometer driving schedule has been employed for this analysis.https://doi.org/10.1049/els2.12020 |
| spellingShingle | Krishna Veer Singh Hari Om Bansal Dheerendra Singh Development of an adaptive neuro‐fuzzy inference system–based equivalent consumption minimisation strategy to improve fuel economy in hybrid electric vehicles IET Electrical Systems in Transportation |
| title | Development of an adaptive neuro‐fuzzy inference system–based equivalent consumption minimisation strategy to improve fuel economy in hybrid electric vehicles |
| title_full | Development of an adaptive neuro‐fuzzy inference system–based equivalent consumption minimisation strategy to improve fuel economy in hybrid electric vehicles |
| title_fullStr | Development of an adaptive neuro‐fuzzy inference system–based equivalent consumption minimisation strategy to improve fuel economy in hybrid electric vehicles |
| title_full_unstemmed | Development of an adaptive neuro‐fuzzy inference system–based equivalent consumption minimisation strategy to improve fuel economy in hybrid electric vehicles |
| title_short | Development of an adaptive neuro‐fuzzy inference system–based equivalent consumption minimisation strategy to improve fuel economy in hybrid electric vehicles |
| title_sort | development of an adaptive neuro fuzzy inference system based equivalent consumption minimisation strategy to improve fuel economy in hybrid electric vehicles |
| url | https://doi.org/10.1049/els2.12020 |
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