Smart City Gnosys
Smart city article details
| Title | Optimization Regenerative Braking In Electric Vehicles Using Q-Learning For Improving Decision-Making In Smart Cities |
|---|---|
| ID_Doc | 40692 |
| Authors | Suanpang P.; Jamjuntr P. |
| Year | 2025 |
| Published | Decision Making: Applications in Management and Engineering, 8, 1 |
| DOI | http://dx.doi.org/10.31181/dmame8120251329 |
| Abstract | The growing prevalence of electric vehicles (EVs) in urban settings underscores the need for advanced decision-making frameworks designed to optimise energy efficiency and improve overall vehicle performance. Regenerative braking, a critical technology in EVs, facilitates energy recovery during deceleration, thereby enhancing efficiency and extending driving range. This study presents an innovative Q-learning-based approach to refine regenerative braking control strategies, aiming to maximise energy recovery, ensure passenger comfort through smooth braking, and maintain safe driving distances. The proposed system leverages real-time feedback on driving patterns, road conditions, and vehicle performance, enabling the Q-learning agent to autonomously adapt its braking strategy for optimal outcomes. By employing Q-learning, the system demonstrates the ability to learn and adjust to dynamic driving environments, progressively enhancing decision-making capabilities. Extensive simulations conducted within a smart city framework revealed substantial improvements in energy efficiency and notable reductions in energy consumption compared to conventional braking systems. The optimisation process incorporated a state space comprising vehicle speed, distance to the preceding vehicle, battery charge level, and road conditions, alongside an action space permitting dynamic braking adjustments. The reward function prioritised maximising energy recovery while minimising jerk and ensuring safety. Simulation outcomes indicated that the Q-learning-based system surpassed traditional control methods, achieving a 15.3% increase in total energy recovered (132.8 kWh), enhanced passenger comfort (jerk reduced to 7.6 m/s3), and a 13% reduction in braking distance. These findings underscore the system's adaptability across varied traffic scenarios. Broader implications include integration into smart city infrastructures, where the adaptive algorithm could enhance real-time traffic management, fostering sustainable urban mobility. Furthermore, the improved energy efficiency reduces overall energy consumption, extends EV range, and decreases charging frequency, aligning with global sustainability objectives. The framework also holds potential for future EV applications, such as adaptive cruise control, autonomous driving, and vehicle-to-grid (V2G) systems. © 2025 Regional Association for Security and crisis management. All rights reserved. |
| Author Keywords | Decision Making; Optimization, Multi-Agent Reinforcement Learning; Smart Grid Management; Vehicle-to-Grid Systems |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
40393  | 0.913 | Suanpang P.; Jamjuntr P. | Optimal Electric Vehicle Battery Management Using Q-Learning For Sustainability | Sustainability (Switzerland), 16, 16 (2024) |
| 40790 | 0.865 | Suanpang P.; Jamjuntr P. | Optimizing Electric Vehicle Charging Recommendation In Smart Cities: A Multi-Agent Reinforcement Learning Approach | World Electric Vehicle Journal, 15, 2 (2024) |
| 4034 | 0.861 | Wang E.; Memar F.H.; Korzelius S.; Sadek A.W.; Qiao C. | A Reinforcement Learning Approach To Cav And Intersection Control For Energy Efficiency | Proceedings - 2022 5th International Conference on Connected and Autonomous Driving, MetroCAD 2022 (2022) |
| 8368 | 0.857 | Dutta P.; Boulanger A.; Anderson R.; Wu L. | An Innovative Approach To Vehicle Electrification For Smart Cities | Handbook of Research on Social, Economic, and Environmental Sustainability in the Development of Smart Cities (2015) |
| 24056 | 0.857 | Aldossary M. | Enhancing Urban Electric Vehicle (Ev) Fleet Management Efficiency In Smart Cities: A Predictive Hybrid Deep Learning Framework | Smart Cities, 7, 6 (2024) |
| 2234 | 0.855 | Chalaki B.; Malikopoulos A.A. | A Hysteretic Q-Learning Coordination Framework For Emerging Mobility Systems In Smart Cities | 2021 European Control Conference, ECC 2021 (2021) |
| 26972 | 0.852 | Breschi V.; Ravazzi C.; Strada S.; Dabbene F.; Tanelli M. | Fostering The Mass Adoption Of Electric Vehicles: A Network-Based Approach | IEEE Transactions on Control of Network Systems, 9, 4 (2022) |
| 51245 | 0.852 | Raja G.; Saravanan G.; Prathiba S.B.; Akhtar Z.; Ali Khowaja S.; Dev K. | Smart Navigation And Energy Management Framework For Autonomous Electric Vehicles In Complex Environments | IEEE Internet of Things Journal, 10, 21 (2023) |
| 23309 | 0.85 | Laroui M.; Dridi A.; Afifi H.; Moungla H.; Marot M.; Cherif M.A. | Energy Management For Electric Vehicles In Smart Cities: A Deep Learning Approach | 2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019 (2019) |