Multi-Objective Optimization of Parking Charging Strategy for Extended-Range Hybrid Electric Vehicle Based on MOMSA
Extended-range hybrid electric vehicles (E-RHEVs) require optimized parking charging strategies that consider both charging time and battery health. Existing research often neglects the crucial impact of ambient temperature and long-term cycling on battery degradation. This study addresses this gap...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/4/203 |
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| author | Rong Yang Jianxiang Lu Zhiqi Sun Wei Huang |
| author_facet | Rong Yang Jianxiang Lu Zhiqi Sun Wei Huang |
| author_sort | Rong Yang |
| collection | DOAJ |
| description | Extended-range hybrid electric vehicles (E-RHEVs) require optimized parking charging strategies that consider both charging time and battery health. Existing research often neglects the crucial impact of ambient temperature and long-term cycling on battery degradation. This study addresses this gap by developing a novel parking charging strategy for E-RHEVs that leverages a temperature-dependent battery aging model and a Multi-Objective Mantis Search Algorithm (MOMSA)—a metaheuristic optimization algorithm designed to solve multi-objective problems by efficiently exploring trade-offs between conflicting objectives. The MOMSA optimizes a five-stage State-of-Charge-based Multi-stage Constant Current (SMCC) charging profile—a dynamic current adjustment strategy that minimizes battery capacity degradation by dividing the charging process into sequential phases. The MOMSA-based SMCC strategy achieves an optimal balance between charging time and battery capacity degradation across a range of ambient temperatures (5 °C to 35 °C). Compared to a conventional 0.5C CC-CV charging strategy, the MOMSA-based SMCC strategy demonstrably reduces battery degradation with a moderate increase in charging time. Furthermore, the MOMSA-based charging strategy outperforms a Multi-Objective Particle Swarm Optimization (MOPSO)-based approach, achieving comparable degradation mitigation while significantly reducing charging time. One-week cycling simulations under realistic driving conditions further validate the MOMSA-based charging strategy’s superior long-term performance in delaying battery degradation across various temperatures. This strategy extends E-RHEV battery lifespan while maintaining operational efficiency. |
| format | Article |
| id | doaj-art-05e200a9e5474db19bb8d11c3695d781 |
| institution | DOAJ |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-05e200a9e5474db19bb8d11c3695d7812025-08-20T03:13:54ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-04-0116420310.3390/wevj16040203Multi-Objective Optimization of Parking Charging Strategy for Extended-Range Hybrid Electric Vehicle Based on MOMSARong Yang0Jianxiang Lu1Zhiqi Sun2Wei Huang3College of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaCollege of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaCollege of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaCollege of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaExtended-range hybrid electric vehicles (E-RHEVs) require optimized parking charging strategies that consider both charging time and battery health. Existing research often neglects the crucial impact of ambient temperature and long-term cycling on battery degradation. This study addresses this gap by developing a novel parking charging strategy for E-RHEVs that leverages a temperature-dependent battery aging model and a Multi-Objective Mantis Search Algorithm (MOMSA)—a metaheuristic optimization algorithm designed to solve multi-objective problems by efficiently exploring trade-offs between conflicting objectives. The MOMSA optimizes a five-stage State-of-Charge-based Multi-stage Constant Current (SMCC) charging profile—a dynamic current adjustment strategy that minimizes battery capacity degradation by dividing the charging process into sequential phases. The MOMSA-based SMCC strategy achieves an optimal balance between charging time and battery capacity degradation across a range of ambient temperatures (5 °C to 35 °C). Compared to a conventional 0.5C CC-CV charging strategy, the MOMSA-based SMCC strategy demonstrably reduces battery degradation with a moderate increase in charging time. Furthermore, the MOMSA-based charging strategy outperforms a Multi-Objective Particle Swarm Optimization (MOPSO)-based approach, achieving comparable degradation mitigation while significantly reducing charging time. One-week cycling simulations under realistic driving conditions further validate the MOMSA-based charging strategy’s superior long-term performance in delaying battery degradation across various temperatures. This strategy extends E-RHEV battery lifespan while maintaining operational efficiency.https://www.mdpi.com/2032-6653/16/4/203parking charging strategyMOMSASMCCmulti-objective optimizationbattery degradation |
| spellingShingle | Rong Yang Jianxiang Lu Zhiqi Sun Wei Huang Multi-Objective Optimization of Parking Charging Strategy for Extended-Range Hybrid Electric Vehicle Based on MOMSA World Electric Vehicle Journal parking charging strategy MOMSA SMCC multi-objective optimization battery degradation |
| title | Multi-Objective Optimization of Parking Charging Strategy for Extended-Range Hybrid Electric Vehicle Based on MOMSA |
| title_full | Multi-Objective Optimization of Parking Charging Strategy for Extended-Range Hybrid Electric Vehicle Based on MOMSA |
| title_fullStr | Multi-Objective Optimization of Parking Charging Strategy for Extended-Range Hybrid Electric Vehicle Based on MOMSA |
| title_full_unstemmed | Multi-Objective Optimization of Parking Charging Strategy for Extended-Range Hybrid Electric Vehicle Based on MOMSA |
| title_short | Multi-Objective Optimization of Parking Charging Strategy for Extended-Range Hybrid Electric Vehicle Based on MOMSA |
| title_sort | multi objective optimization of parking charging strategy for extended range hybrid electric vehicle based on momsa |
| topic | parking charging strategy MOMSA SMCC multi-objective optimization battery degradation |
| url | https://www.mdpi.com/2032-6653/16/4/203 |
| work_keys_str_mv | AT rongyang multiobjectiveoptimizationofparkingchargingstrategyforextendedrangehybridelectricvehiclebasedonmomsa AT jianxianglu multiobjectiveoptimizationofparkingchargingstrategyforextendedrangehybridelectricvehiclebasedonmomsa AT zhiqisun multiobjectiveoptimizationofparkingchargingstrategyforextendedrangehybridelectricvehiclebasedonmomsa AT weihuang multiobjectiveoptimizationofparkingchargingstrategyforextendedrangehybridelectricvehiclebasedonmomsa |