Driving-Cycle-Adaptive Energy Management Strategy for Hybrid Energy Storage Electric Vehicles
The energy management strategy (EMS) is a critical technology for pure electric vehicles equipped with hybrid energy storage systems. This study addresses the challenges of limited adaptability to driving cycles and significant battery capacity degradation in lithium battery–supercapacitor hybrid en...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/6/313 |
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| author | Zhaocheng Lu Tiezhu Zhang Rui Li Xinyu Ni |
| author_facet | Zhaocheng Lu Tiezhu Zhang Rui Li Xinyu Ni |
| author_sort | Zhaocheng Lu |
| collection | DOAJ |
| description | The energy management strategy (EMS) is a critical technology for pure electric vehicles equipped with hybrid energy storage systems. This study addresses the challenges of limited adaptability to driving cycles and significant battery capacity degradation in lithium battery–supercapacitor hybrid energy storage systems by proposing an adaptive EMS based on Dynamic Programming-Optimized Control Rules (DP-OCR). Dynamic programming is employed to optimize the rule-based control strategy, while the grey wolf optimizer (GWO) is utilized to enhance the least squares support vector machine (LSSVM) driving cycle recognition model. The optimized driving cycle recognition model is integrated with the improved rule-based control strategy, facilitating adaptive adjustment of control parameters based on driving cycle identification results. This integration enables optimal power distribution between lithium batteries and supercapacitors, thereby improving the EMS’s adaptability to varying driving conditions and extending battery lifespan. Simulation results under complex driving cycles indicate that, compared to conventional deterministic rule-based EMS and single-battery vehicles, the proposed DP-OCR-based adaptive EMS reduces overall energy consumption by 8.29% and 17.48%, respectively. |
| format | Article |
| id | doaj-art-4eb1d896a8bb4d828eddba38863abc01 |
| institution | Kabale University |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-4eb1d896a8bb4d828eddba38863abc012025-08-20T03:29:43ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-06-0116631310.3390/wevj16060313Driving-Cycle-Adaptive Energy Management Strategy for Hybrid Energy Storage Electric VehiclesZhaocheng Lu0Tiezhu Zhang1Rui Li2Xinyu Ni3School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, ChinaSchool of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, ChinaSchool of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, ChinaSchool of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, ChinaThe energy management strategy (EMS) is a critical technology for pure electric vehicles equipped with hybrid energy storage systems. This study addresses the challenges of limited adaptability to driving cycles and significant battery capacity degradation in lithium battery–supercapacitor hybrid energy storage systems by proposing an adaptive EMS based on Dynamic Programming-Optimized Control Rules (DP-OCR). Dynamic programming is employed to optimize the rule-based control strategy, while the grey wolf optimizer (GWO) is utilized to enhance the least squares support vector machine (LSSVM) driving cycle recognition model. The optimized driving cycle recognition model is integrated with the improved rule-based control strategy, facilitating adaptive adjustment of control parameters based on driving cycle identification results. This integration enables optimal power distribution between lithium batteries and supercapacitors, thereby improving the EMS’s adaptability to varying driving conditions and extending battery lifespan. Simulation results under complex driving cycles indicate that, compared to conventional deterministic rule-based EMS and single-battery vehicles, the proposed DP-OCR-based adaptive EMS reduces overall energy consumption by 8.29% and 17.48%, respectively.https://www.mdpi.com/2032-6653/16/6/313hybrid energy storage systemdynamic programmingleast squares support vector machinedriving cycle recognitionenergy management |
| spellingShingle | Zhaocheng Lu Tiezhu Zhang Rui Li Xinyu Ni Driving-Cycle-Adaptive Energy Management Strategy for Hybrid Energy Storage Electric Vehicles World Electric Vehicle Journal hybrid energy storage system dynamic programming least squares support vector machine driving cycle recognition energy management |
| title | Driving-Cycle-Adaptive Energy Management Strategy for Hybrid Energy Storage Electric Vehicles |
| title_full | Driving-Cycle-Adaptive Energy Management Strategy for Hybrid Energy Storage Electric Vehicles |
| title_fullStr | Driving-Cycle-Adaptive Energy Management Strategy for Hybrid Energy Storage Electric Vehicles |
| title_full_unstemmed | Driving-Cycle-Adaptive Energy Management Strategy for Hybrid Energy Storage Electric Vehicles |
| title_short | Driving-Cycle-Adaptive Energy Management Strategy for Hybrid Energy Storage Electric Vehicles |
| title_sort | driving cycle adaptive energy management strategy for hybrid energy storage electric vehicles |
| topic | hybrid energy storage system dynamic programming least squares support vector machine driving cycle recognition energy management |
| url | https://www.mdpi.com/2032-6653/16/6/313 |
| work_keys_str_mv | AT zhaochenglu drivingcycleadaptiveenergymanagementstrategyforhybridenergystorageelectricvehicles AT tiezhuzhang drivingcycleadaptiveenergymanagementstrategyforhybridenergystorageelectricvehicles AT ruili drivingcycleadaptiveenergymanagementstrategyforhybridenergystorageelectricvehicles AT xinyuni drivingcycleadaptiveenergymanagementstrategyforhybridenergystorageelectricvehicles |