Advanced Adaptive Rule-Based Energy Management for Hybrid Energy Storage Systems (HESSs) to Enhance the Driving Range of Electric Vehicles
The energy storage system (ESS) plays a crucial role in electric vehicles (EVs), impacting their performance and efficiency. While batteries are the standard choice for energy storage, they come with drawbacks like low power density and limited life cycles, which can hinder pure battery electric veh...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Vehicles |
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| Online Access: | https://www.mdpi.com/2624-8921/7/1/6 |
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| author | Chew Kuew Wai Taha Sadeq Lee Cheun Hau |
| author_facet | Chew Kuew Wai Taha Sadeq Lee Cheun Hau |
| author_sort | Chew Kuew Wai |
| collection | DOAJ |
| description | The energy storage system (ESS) plays a crucial role in electric vehicles (EVs), impacting their performance and efficiency. While batteries are the standard choice for energy storage, they come with drawbacks like low power density and limited life cycles, which can hinder pure battery electric vehicles (PBEVs). To address these issues, a hybrid energy storage system (HESS) that combines a battery with a supercapacitor provides a more effective solution. The battery delivers consistent power, while the supercapacitor manages peak power demands and regenerative braking energy. This study proposes a new energy management strategy for the HESS, an advanced adaptive rule-based algorithm. The results of the standard rule-based and adaptive rule-based algorithms are used to verify the proposed control algorithm. The system was modeled in MATLAB/Simulink and evaluated across three driving cycles—UDDS, NYCC, and Japan1015—while varying states of charge for the supercapacitors. The findings indicate that the HESS significantly alleviates battery stress compared to a pure battery system, enhancing both efficiency and lifespan. Among the algorithms tested, the advanced adaptive rule-based algorithm yielded the best results, increasing the number of viable drive cycles. |
| format | Article |
| id | doaj-art-427c7ef264814d2bb66d57873cb2a46f |
| institution | Kabale University |
| issn | 2624-8921 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vehicles |
| spelling | doaj-art-427c7ef264814d2bb66d57873cb2a46f2025-08-20T03:43:58ZengMDPI AGVehicles2624-89212025-01-0171610.3390/vehicles7010006Advanced Adaptive Rule-Based Energy Management for Hybrid Energy Storage Systems (HESSs) to Enhance the Driving Range of Electric VehiclesChew Kuew Wai0Taha Sadeq1Lee Cheun Hau2Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahma, Sungai Long Campus, Kajang 43000, Selangor, MalaysiaFaculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja Campus, Parit Raja 86400, Johor, MalaysiaLee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahma, Sungai Long Campus, Kajang 43000, Selangor, MalaysiaThe energy storage system (ESS) plays a crucial role in electric vehicles (EVs), impacting their performance and efficiency. While batteries are the standard choice for energy storage, they come with drawbacks like low power density and limited life cycles, which can hinder pure battery electric vehicles (PBEVs). To address these issues, a hybrid energy storage system (HESS) that combines a battery with a supercapacitor provides a more effective solution. The battery delivers consistent power, while the supercapacitor manages peak power demands and regenerative braking energy. This study proposes a new energy management strategy for the HESS, an advanced adaptive rule-based algorithm. The results of the standard rule-based and adaptive rule-based algorithms are used to verify the proposed control algorithm. The system was modeled in MATLAB/Simulink and evaluated across three driving cycles—UDDS, NYCC, and Japan1015—while varying states of charge for the supercapacitors. The findings indicate that the HESS significantly alleviates battery stress compared to a pure battery system, enhancing both efficiency and lifespan. Among the algorithms tested, the advanced adaptive rule-based algorithm yielded the best results, increasing the number of viable drive cycles.https://www.mdpi.com/2624-8921/7/1/6batteriessupercapacitorsadaptive algorithmhybrid energy storageelectric vehicle |
| spellingShingle | Chew Kuew Wai Taha Sadeq Lee Cheun Hau Advanced Adaptive Rule-Based Energy Management for Hybrid Energy Storage Systems (HESSs) to Enhance the Driving Range of Electric Vehicles Vehicles batteries supercapacitors adaptive algorithm hybrid energy storage electric vehicle |
| title | Advanced Adaptive Rule-Based Energy Management for Hybrid Energy Storage Systems (HESSs) to Enhance the Driving Range of Electric Vehicles |
| title_full | Advanced Adaptive Rule-Based Energy Management for Hybrid Energy Storage Systems (HESSs) to Enhance the Driving Range of Electric Vehicles |
| title_fullStr | Advanced Adaptive Rule-Based Energy Management for Hybrid Energy Storage Systems (HESSs) to Enhance the Driving Range of Electric Vehicles |
| title_full_unstemmed | Advanced Adaptive Rule-Based Energy Management for Hybrid Energy Storage Systems (HESSs) to Enhance the Driving Range of Electric Vehicles |
| title_short | Advanced Adaptive Rule-Based Energy Management for Hybrid Energy Storage Systems (HESSs) to Enhance the Driving Range of Electric Vehicles |
| title_sort | advanced adaptive rule based energy management for hybrid energy storage systems hesss to enhance the driving range of electric vehicles |
| topic | batteries supercapacitors adaptive algorithm hybrid energy storage electric vehicle |
| url | https://www.mdpi.com/2624-8921/7/1/6 |
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