Enhancing energy management in battery electric vehicles: A novel approach based on fuzzy Q-learning controller
The global emphasis on sustainable transportation is driving the increasing adoption of Battery Electric Vehicles (BEVs), which offer independence from fossil fuels and zero emissions during operation. However, optimizing energy efficiency and vehicle performance in BEVs remains a significant challe...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Engineering Science and Technology, an International Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098625001259 |
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| author | Salma Ariche Zakaria Boulghasoul Abdelhafid El Ouardi Abdelhadi Elbacha Abdelouahed Tajer Stéphane Espié |
| author_facet | Salma Ariche Zakaria Boulghasoul Abdelhafid El Ouardi Abdelhadi Elbacha Abdelouahed Tajer Stéphane Espié |
| author_sort | Salma Ariche |
| collection | DOAJ |
| description | The global emphasis on sustainable transportation is driving the increasing adoption of Battery Electric Vehicles (BEVs), which offer independence from fossil fuels and zero emissions during operation. However, optimizing energy efficiency and vehicle performance in BEVs remains a significant challenge due to the dynamic nature of driving conditions. Current power control methods often struggle to adapt to these varying conditions, leading to suboptimal energy distribution and reduced performance. This paper presents a novel approach to power control in BEVs using a Fuzzy Q-learning Controller (FQLC), which dynamically adjusts the motor power coefficient based on real-time driving conditions. The FQLC optimizes energy distribution to the electric motor by adapting to factors such as vehicle speed, road slope, and battery state of charge (SOC). A comparative analysis between the Fuzzy Logic Controller (FLC) and the proposed FQLC demonstrates the advantages of the new system. The Modified Mean Absolute Error (MMAE) is used to quantitatively evaluate performance across various driving scenarios. The results show that the FQLC significantly outperforms the FLC, achieving MMAE values as low as 0.01, indicating substantial reductions in error rates. In the performed tests, the FQLC’s ability to manage energy use contributed to range extensions in certain cases, achieving an increase of up to 11 km. These findings highlight the FQLC potential as an innovative solution for BEV power control. |
| format | Article |
| id | doaj-art-0dbff42e8ffa4febb00dc51d2e3f4ce9 |
| institution | DOAJ |
| issn | 2215-0986 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj-art-0dbff42e8ffa4febb00dc51d2e3f4ce92025-08-20T03:05:42ZengElsevierEngineering Science and Technology, an International Journal2215-09862025-07-016710207010.1016/j.jestch.2025.102070Enhancing energy management in battery electric vehicles: A novel approach based on fuzzy Q-learning controllerSalma Ariche0Zakaria Boulghasoul1Abdelhafid El Ouardi2Abdelhadi Elbacha3Abdelouahed Tajer4Stéphane Espié5Université Paris-Saclay, ENS Paris-Saclay, CNRS, SATIE, Gif-sur-Yvette, 91110, France; Cadi Ayyad University, National School of Applied Sciences, Engineering Systems and Applications (LISA), Marrakech, 40000, Morocco; Corresponding author at: Université Paris-Saclay, ENS Paris-Saclay, CNRS, SATIE, Gif-sur-Yvette, 91110, France.Cadi Ayyad University, National School of Applied Sciences, Engineering Systems and Applications (LISA), Marrakech, 40000, MoroccoUniversité Paris-Saclay, ENS Paris-Saclay, CNRS, SATIE, Gif-sur-Yvette, 91110, FranceCadi Ayyad University, National School of Applied Sciences, Engineering Systems and Applications (LISA), Marrakech, 40000, MoroccoCadi Ayyad University, National School of Applied Sciences, Engineering Systems and Applications (LISA), Marrakech, 40000, MoroccoUniversité Gustave Eiffel, SATIE, 91190 Gif-sur-Yvette, FranceThe global emphasis on sustainable transportation is driving the increasing adoption of Battery Electric Vehicles (BEVs), which offer independence from fossil fuels and zero emissions during operation. However, optimizing energy efficiency and vehicle performance in BEVs remains a significant challenge due to the dynamic nature of driving conditions. Current power control methods often struggle to adapt to these varying conditions, leading to suboptimal energy distribution and reduced performance. This paper presents a novel approach to power control in BEVs using a Fuzzy Q-learning Controller (FQLC), which dynamically adjusts the motor power coefficient based on real-time driving conditions. The FQLC optimizes energy distribution to the electric motor by adapting to factors such as vehicle speed, road slope, and battery state of charge (SOC). A comparative analysis between the Fuzzy Logic Controller (FLC) and the proposed FQLC demonstrates the advantages of the new system. The Modified Mean Absolute Error (MMAE) is used to quantitatively evaluate performance across various driving scenarios. The results show that the FQLC significantly outperforms the FLC, achieving MMAE values as low as 0.01, indicating substantial reductions in error rates. In the performed tests, the FQLC’s ability to manage energy use contributed to range extensions in certain cases, achieving an increase of up to 11 km. These findings highlight the FQLC potential as an innovative solution for BEV power control.http://www.sciencedirect.com/science/article/pii/S2215098625001259Battery electric vehiclesEnergy managementFuzzy logicFuzzy q-learningReinforcement learning |
| spellingShingle | Salma Ariche Zakaria Boulghasoul Abdelhafid El Ouardi Abdelhadi Elbacha Abdelouahed Tajer Stéphane Espié Enhancing energy management in battery electric vehicles: A novel approach based on fuzzy Q-learning controller Engineering Science and Technology, an International Journal Battery electric vehicles Energy management Fuzzy logic Fuzzy q-learning Reinforcement learning |
| title | Enhancing energy management in battery electric vehicles: A novel approach based on fuzzy Q-learning controller |
| title_full | Enhancing energy management in battery electric vehicles: A novel approach based on fuzzy Q-learning controller |
| title_fullStr | Enhancing energy management in battery electric vehicles: A novel approach based on fuzzy Q-learning controller |
| title_full_unstemmed | Enhancing energy management in battery electric vehicles: A novel approach based on fuzzy Q-learning controller |
| title_short | Enhancing energy management in battery electric vehicles: A novel approach based on fuzzy Q-learning controller |
| title_sort | enhancing energy management in battery electric vehicles a novel approach based on fuzzy q learning controller |
| topic | Battery electric vehicles Energy management Fuzzy logic Fuzzy q-learning Reinforcement learning |
| url | http://www.sciencedirect.com/science/article/pii/S2215098625001259 |
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