Study of HEV power management control strategy based on driving pattern recognition
In this work, an optimized HEV power management fuzzy control strategy is proposed with the aim to further improve the fuel efficiency of the rule-based control strategy and overcome the drawbacks of the conventional control strategies. The driving pattern recognition method is used to classify the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2016
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/53307/ |
| _version_ | 1848798919569440768 |
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| author | Wei, Zhen Xu, Z. Halim, Dunant |
| author_facet | Wei, Zhen Xu, Z. Halim, Dunant |
| author_sort | Wei, Zhen |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | In this work, an optimized HEV power management fuzzy control strategy is proposed with the aim to further improve the fuel efficiency of the rule-based control strategy and overcome the drawbacks of the conventional control strategies. The driving pattern recognition method is used to classify the driving condition into one of the driving patterns to select proper control algorithm. The dynamic programming solution is used to design the fuzzy control strategies for each driving pattern. The simulation results indicate that by adopting the proposed strategy the fuel efficiency of HEV is improved, especially under complex driving conditions. |
| first_indexed | 2025-11-14T20:27:25Z |
| format | Article |
| id | nottingham-53307 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:27:25Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-533072018-08-10T08:14:54Z https://eprints.nottingham.ac.uk/53307/ Study of HEV power management control strategy based on driving pattern recognition Wei, Zhen Xu, Z. Halim, Dunant In this work, an optimized HEV power management fuzzy control strategy is proposed with the aim to further improve the fuel efficiency of the rule-based control strategy and overcome the drawbacks of the conventional control strategies. The driving pattern recognition method is used to classify the driving condition into one of the driving patterns to select proper control algorithm. The dynamic programming solution is used to design the fuzzy control strategies for each driving pattern. The simulation results indicate that by adopting the proposed strategy the fuel efficiency of HEV is improved, especially under complex driving conditions. Elsevier 2016-06-17 Article PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/53307/1/1-s2.0-S1876610216301266-main.pdf Wei, Zhen, Xu, Z. and Halim, Dunant (2016) Study of HEV power management control strategy based on driving pattern recognition. Energy Procedia, 88 . pp. 847-853. ISSN 1876-6102 HEV; driving cycle; energy management; fuzzy logic control https://doi.org/10.1016/j.egypro.2016.06.062 doi:10.1016/j.egypro.2016.06.062 doi:10.1016/j.egypro.2016.06.062 |
| spellingShingle | HEV; driving cycle; energy management; fuzzy logic control Wei, Zhen Xu, Z. Halim, Dunant Study of HEV power management control strategy based on driving pattern recognition |
| title | Study of HEV power management control strategy based on driving pattern recognition |
| title_full | Study of HEV power management control strategy based on driving pattern recognition |
| title_fullStr | Study of HEV power management control strategy based on driving pattern recognition |
| title_full_unstemmed | Study of HEV power management control strategy based on driving pattern recognition |
| title_short | Study of HEV power management control strategy based on driving pattern recognition |
| title_sort | study of hev power management control strategy based on driving pattern recognition |
| topic | HEV; driving cycle; energy management; fuzzy logic control |
| url | https://eprints.nottingham.ac.uk/53307/ https://eprints.nottingham.ac.uk/53307/ https://eprints.nottingham.ac.uk/53307/ |