RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions
Abstract This paper presents a new intelligent power management strategy based on multi-objective cost function for plug-in biogas hybrid vehicles (PBHVs). This strategy consists of long-term power management and a short-term controller. The long-term power management depends on an improved generali...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2022-11-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-022-00890-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850109872243212288 |
|---|---|
| author | Sameh Abd-Elhaleem Walaa Shoeib Abdel Azim Sobaih |
| author_facet | Sameh Abd-Elhaleem Walaa Shoeib Abdel Azim Sobaih |
| author_sort | Sameh Abd-Elhaleem |
| collection | DOAJ |
| description | Abstract This paper presents a new intelligent power management strategy based on multi-objective cost function for plug-in biogas hybrid vehicles (PBHVs). This strategy consists of long-term power management and a short-term controller. The long-term power management depends on an improved generalized particle swarm optimization algorithm (IGPSO) to obtain the globally optimal values of motor and biogas engine torques. To reduce the computation time, five-mode rule-based control is used, where the IGPSO estimates the optimal values for the motor and engine torques in a hybrid mode depending on a multi-objective cost function. This cost function aims to reduce fuel consumption and the drawn current from the battery and takes into consideration the battery ageing. The short-term controller is designed using an interval type-2 Takagi–Sugeno-Kang (IT2TSK) fuzzy algorithm, which depends on human experts to overcome the uncertainties of the driving conditions. Lyapunov stability theory for the online controller is proved. The proposed technique improves the energy consumption compared to other techniques. The simulation results using real data for the engine, motor and battery illustrate the feasibility and effectiveness of the proposed approach with comparative results. |
| format | Article |
| id | doaj-art-7358bc0edef04b2393cffc7a2fa7d7b6 |
| institution | OA Journals |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-7358bc0edef04b2393cffc7a2fa7d7b62025-08-20T02:37:58ZengSpringerComplex & Intelligent Systems2199-45362198-60532022-11-01933115313010.1007/s40747-022-00890-8RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditionsSameh Abd-Elhaleem0Walaa Shoeib1Abdel Azim Sobaih2Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia UniversityDepartment of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia UniversityDepartment of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia UniversityAbstract This paper presents a new intelligent power management strategy based on multi-objective cost function for plug-in biogas hybrid vehicles (PBHVs). This strategy consists of long-term power management and a short-term controller. The long-term power management depends on an improved generalized particle swarm optimization algorithm (IGPSO) to obtain the globally optimal values of motor and biogas engine torques. To reduce the computation time, five-mode rule-based control is used, where the IGPSO estimates the optimal values for the motor and engine torques in a hybrid mode depending on a multi-objective cost function. This cost function aims to reduce fuel consumption and the drawn current from the battery and takes into consideration the battery ageing. The short-term controller is designed using an interval type-2 Takagi–Sugeno-Kang (IT2TSK) fuzzy algorithm, which depends on human experts to overcome the uncertainties of the driving conditions. Lyapunov stability theory for the online controller is proved. The proposed technique improves the energy consumption compared to other techniques. The simulation results using real data for the engine, motor and battery illustrate the feasibility and effectiveness of the proposed approach with comparative results.https://doi.org/10.1007/s40747-022-00890-8Plug-in biogas hybrid vehiclesEnergy management strategyImproved generalized particle swarm optimization algorithmState of chargeInterval type-2 Takagi–Sugeno-Kang |
| spellingShingle | Sameh Abd-Elhaleem Walaa Shoeib Abdel Azim Sobaih RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions Complex & Intelligent Systems Plug-in biogas hybrid vehicles Energy management strategy Improved generalized particle swarm optimization algorithm State of charge Interval type-2 Takagi–Sugeno-Kang |
| title | RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions |
| title_full | RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions |
| title_fullStr | RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions |
| title_full_unstemmed | RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions |
| title_short | RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions |
| title_sort | retracted article intelligent power management based on multi objective cost function for plug in biogas hybrid vehicles under uncertain driving conditions |
| topic | Plug-in biogas hybrid vehicles Energy management strategy Improved generalized particle swarm optimization algorithm State of charge Interval type-2 Takagi–Sugeno-Kang |
| url | https://doi.org/10.1007/s40747-022-00890-8 |
| work_keys_str_mv | AT samehabdelhaleem retractedarticleintelligentpowermanagementbasedonmultiobjectivecostfunctionforpluginbiogashybridvehiclesunderuncertaindrivingconditions AT walaashoeib retractedarticleintelligentpowermanagementbasedonmultiobjectivecostfunctionforpluginbiogashybridvehiclesunderuncertaindrivingconditions AT abdelazimsobaih retractedarticleintelligentpowermanagementbasedonmultiobjectivecostfunctionforpluginbiogashybridvehiclesunderuncertaindrivingconditions |