Optimal Charging Strategy for Plug-in Hybrid Electric Vehicle Using Evolutionary Algorithm
Plug-in Hybrid Electric Vehicle (PHEV) has gained immense popularity ever since it offers many advantages as compared to conventional internal combustion engine (ICE) vehicle. One millions of PHEVs are estimated to be in the USA market by 2015. Uncoordinated PHEV charging will cause significant impa...
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| Formato: | Conference or Workshop Item |
| Idioma: | English |
| Publicado em: |
2014
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| Acesso em linha: | http://umpir.ump.edu.my/id/eprint/6443/1/Optimal_charging_strategy_for_Plug-in_Hybrid_Electric_Vehicle_using_evolutionary_algorithm.pdf |
| _version_ | 1825821774383677440 |
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| author | Mohd Redzuan, Ahmad Ismail, Musirin M. M., Othman |
| author_facet | Mohd Redzuan, Ahmad Ismail, Musirin M. M., Othman |
| author_sort | Mohd Redzuan, Ahmad |
| collection | UMP |
| description | Plug-in Hybrid Electric Vehicle (PHEV) has gained immense popularity ever since it offers many advantages as compared to conventional internal combustion engine (ICE) vehicle. One millions of PHEVs are estimated to be in the USA market by 2015. Uncoordinated PHEV charging will cause significant impacts to the power grid; i.e. lines and transformers overload and voltage drops. Appropriate charging methods should be used to minimize the impacts of PHEV charging activities and at the same time minimize daily charging cost. This paper presents methods used to charge the PHEV battery namely price-based charging, load-based charging and SOC-based charging. Evolutionary programming (EP) is then used to optimize the charging rate and SOC thus minimizing the charging cost. Charging cost is calculated based on real time electricity price i.e. Locational Marginal Price (LMP). Since the data pattern for LMP is similar throughout the week, the day-ahead price model is used to calculate charging cost. Results from the study indicated that charging strategies used produces different impacts to the grid. Moreover charging cost may vary from one method to another. Optimization of charging rate and SOC hence minimized charging cost is done by EP. |
| first_indexed | 2024-03-06T11:46:36Z |
| format | Conference or Workshop Item |
| id | UMPir6443 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T11:46:36Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | UMPir64432018-02-05T01:03:01Z http://umpir.ump.edu.my/id/eprint/6443/ Optimal Charging Strategy for Plug-in Hybrid Electric Vehicle Using Evolutionary Algorithm Mohd Redzuan, Ahmad Ismail, Musirin M. M., Othman TK Electrical engineering. Electronics Nuclear engineering Plug-in Hybrid Electric Vehicle (PHEV) has gained immense popularity ever since it offers many advantages as compared to conventional internal combustion engine (ICE) vehicle. One millions of PHEVs are estimated to be in the USA market by 2015. Uncoordinated PHEV charging will cause significant impacts to the power grid; i.e. lines and transformers overload and voltage drops. Appropriate charging methods should be used to minimize the impacts of PHEV charging activities and at the same time minimize daily charging cost. This paper presents methods used to charge the PHEV battery namely price-based charging, load-based charging and SOC-based charging. Evolutionary programming (EP) is then used to optimize the charging rate and SOC thus minimizing the charging cost. Charging cost is calculated based on real time electricity price i.e. Locational Marginal Price (LMP). Since the data pattern for LMP is similar throughout the week, the day-ahead price model is used to calculate charging cost. Results from the study indicated that charging strategies used produces different impacts to the grid. Moreover charging cost may vary from one method to another. Optimization of charging rate and SOC hence minimized charging cost is done by EP. 2014 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/6443/1/Optimal_charging_strategy_for_Plug-in_Hybrid_Electric_Vehicle_using_evolutionary_algorithm.pdf Mohd Redzuan, Ahmad and Ismail, Musirin and M. M., Othman (2014) Optimal Charging Strategy for Plug-in Hybrid Electric Vehicle Using Evolutionary Algorithm. In: IEEE 8th International Power Engineering and Optimization Conference (PEOCO 2014) , 24-25 March 2014 , Langkawi. pp. 557-562.. (Published) http://dx.doi.org/10.1109/PEOCO.2014.6814491 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mohd Redzuan, Ahmad Ismail, Musirin M. M., Othman Optimal Charging Strategy for Plug-in Hybrid Electric Vehicle Using Evolutionary Algorithm |
| title | Optimal Charging Strategy for Plug-in Hybrid Electric Vehicle Using Evolutionary Algorithm |
| title_full | Optimal Charging Strategy for Plug-in Hybrid Electric Vehicle Using Evolutionary Algorithm |
| title_fullStr | Optimal Charging Strategy for Plug-in Hybrid Electric Vehicle Using Evolutionary Algorithm |
| title_full_unstemmed | Optimal Charging Strategy for Plug-in Hybrid Electric Vehicle Using Evolutionary Algorithm |
| title_short | Optimal Charging Strategy for Plug-in Hybrid Electric Vehicle Using Evolutionary Algorithm |
| title_sort | optimal charging strategy for plug in hybrid electric vehicle using evolutionary algorithm |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/6443/1/Optimal_charging_strategy_for_Plug-in_Hybrid_Electric_Vehicle_using_evolutionary_algorithm.pdf |
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