Optimization Regenerative Braking in Electric Vehicles Using Q-Learning for Improving Decision-Making in Smart Cities

Pannee Suanpang; Pitchaya Jamjuntr

doi:10.31181/dmame8120251329

Authors

Pannee Suanpang Department of Information Technology, Faculty of Science & Technology, Suan Dusit University, Bangkok, Thailand. https://orcid.org/0000-0002-0059-2603
Pitchaya Jamjuntr Department of Electrical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand. https://orcid.org/0009-0006-4199-4197

DOI:

https://doi.org/10.31181/dmame8120251329

Keywords:

Decision Making; Smart Grid Management; Optimization, Multi-Agent Reinforcement Learning; Vehicle-to-Grid Systems

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

Downloads

Download data is not yet available.

References

[1] Vaidya, B. and H.T. Mouftah. (2020). Smart electric vehicle charging management for smart cities. IET Smart Cities, 2(1), 4-13. https://doi.org/10.1049/iet-smc.2019.0076

[2] Bilal, M. and M. Rizwan. (2023). Intelligent algorithm-based efficient planning of electric vehicle charging station: a case study of metropolitan city of India. Scientia Iranica, 30(2), 559-576. https://doi.org/10.24200/sci.2021.57433.5238

[3] Haghani, M., et al. (2023). Trends in electric vehicles research. Transportation research part D: transport and environment, 123, 103881. https://doi.org/10.1016/j.trd.2023.103881

[4] Bauer, C., et al. (2015). The environmental performance of current and future passenger vehicles: Life cycle assessment based on a novel scenario analysis framework. Applied energy, 157, 871-883. https://doi.org/10.1016/j.apenergy.2015.01.019

[5] Keshavarz-Ghorabaee, M. (2024). Application of a decision-making approach based on factor analysis and DEMATEL for evaluating challenges of adopting electric vehicles. The Open Transportation Journal, 18(1). http://dx.doi.org/10.2174/0126671212332468240829052532

[6] Chen, Z., et al. (2021). Environmental and economic impact of electric vehicle adoption in the US. Environmental Research Letters, 16(4), 045011. https://doi.org/10.1088/1748-9326/abe2d0

[7] Muratori, M., et al. (2021). The rise of electric vehicles—2020 status and future expectations. Progress in Energy, 3(2), 022002. https://doi.org/10.1088/2516-1083/abe0ad

[8] Rashidizadeh-Kermani, H., et al. (2018). Optimal decision making framework of an electric vehicle aggregator in future and pool markets. Journal of Operation and Automation in Power Engineering, 6(2), 157-168. https://doi.org/10.22098/joape.2006.3608.1288

[9] Zhu, P., et al., Looking forward: The long-term implications of COVID-19 for transportation. 2023, Elsevier. p. 103910. https://doi.org/10.1016/j.trd.2023.103910

[10] Vodovozov, V., Z. Raud, and E. Petlenkov. (2021). Review on braking energy management in electric vehicles. Energies, 14(15), 4477. https://doi.org/10.3390/en14154477

[11] Yang, C., et al. (2024). Regenerative braking system development and perspectives for electric vehicles: An overview. Renewable and Sustainable Energy Reviews, 198, 114389. https://doi.org/10.1016/j.rser.2024.114389

[12] Jansen, S., M. Alirezaei, and S. Kanarachos, Adaptive regenerative braking for electric vehicles with an electric motor at the front axle using the state dependent riccati equation control technique. 2014: World Scientific and Engineering Academy and Society. https://doi.org/10.1177/0954407013498227

[13] Li, W., et al. (2024). Regenerative braking control strategy for pure electric vehicles based on fuzzy neural network. Ain Shams Engineering Journal, 15(2), 102430. https://doi.org/10.1016/j.asej.2023.102430

[14] Ziadia, M., et al. (2023). An adaptive regenerative braking strategy design based on naturalistic regeneration performance for intelligent vehicles. IEEE Access, 11, 99573-99588. https://doi.org/10.1109/ACCESS.2023.3313553

[15] Kotov, E.V. and K.M. Raju. Real-time adaptive control of electric vehicle drives using artificial neural networks. in MATEC Web of Conferences. 2024. EDP Sciences. https://doi.org/10.1051/matecconf/202439201178

[16] Sutton, R.S. and A.G. Barto. (2018). Reinforcement learning: An introduction. https://psycnet.apa.org/record/2019-19679-000

[17] Mnih, V., et al. (2015). Human-level control through deep reinforcement learning. nature, 518(7540), 529-533. https://doi.org/10.1038/nature14236

[18] Van Hasselt, H., A. Guez, and D. Silver. Deep reinforcement learning with double q-learning. in Proceedings of the AAAI conference on artificial intelligence. 2016. https://doi.org/10.1609/aaai.v30i1.10295

[19] Watkins, C.J.C.H. and P. Dayan. (1992). Technical Note: Q-Learning. Machine Learning, 8(3), 279-292. https://doi.org/10.1023/A:1022676722315

[20] Schulman, J., et al. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347. https://doi.org/10.48550/arXiv.1707.06347

[21] Zakeri, S., et al. (2024). A Machine Learning-Based Decision Analytic Model for Optimal Route Selection in Autonomous Urban Delivery: The ULTIMO Project. Decision Making: Applications in Management and Engineering, 7(2), 572-590. https://doi.org/10.31181/dmame7220241114

[22] Panagiotidis, M., G. Delagrammatikas, and D. Assanis. (2000). Development and use of a regenerative braking model for a parallel hybrid electric vehicle. SAE transactions, 1180-1191. https://www.jstor.org/stable/44634297

[23] Armenta-Déu, C. and H. Cortés. (2023). Analysis of kinetic energy recovery systems in electric vehicles. Vehicles, 5(2), 387-403. https://doi.org/10.3390/vehicles5020022

[24] Jamadar, N.M. and H. Jadhav. (2021). Rule-based assistive hybrid electric brake system with energy generation for electric vehicle. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1-19. https://doi.org/10.1080/15567036.2021.2015485

[25] Yu, R., Y. Zheng, and X. Qu. (2021). Dynamic driving environment complexity quantification method and its verification. Transportation Research Part C: Emerging Technologies, 127, 103051. https://doi.org/10.1016/j.trc.2021.103051

[26] Schreier, M., V. Willert, and J. Adamy. (2015). Compact representation of dynamic driving environments for ADAS by parametric free space and dynamic object maps. IEEE Transactions on Intelligent Transportation Systems, 17(2), 367-384. https://doi.org/10.1109/TITS.2015.2472965

[27] Zhang, J., et al. (2020). Regenerative braking control method based on predictive optimization for four-wheel drive pure electric vehicle. IEEE Access, 9, 1394-1406. https://doi.org/10.1109/ACCESS.2020.3046853

[28] Salari, A.H., H. Mirzaeinejad, and M.F. Mahani. (2023). A new control algorithm of regenerative braking management for energy efficiency and safety enhancement of electric vehicles. Energy Conversion and Management, 276, 116564. https://doi.org/10.1016/j.enconman.2022.116564

[29] Indu, K. and M.A. Kumar. (2023). Design and performance analysis of braking system in an electric vehicle using adaptive neural networks. Sustainable Energy, Grids and Networks, 36, 101215. https://doi.org/10.1016/j.segan.2023.101215

[30] Zheng, C., et al. (2022). Reinforcement learning-based energy management strategies of fuel cell hybrid vehicles with multi-objective control. Journal of Power Sources, 543, 231841. https://doi.org/10.1016/j.jpowsour.2022.231841

[31] Singh, K.V., H.O. Bansal, and D. Singh. (2019). A comprehensive review on hybrid electric vehicles: architectures and components. Journal of Modern Transportation, 27(2), 77-107. https://doi.org/10.1007/s40534-019-0184-

[32] Chandak, G.A. and A. Bhole. (2017). A review on regenerative braking in electric vehicle. 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), 1-5. https://doi.org/10.1109/IPACT.2017.8245098

[33] Li, C., et al. (2024). Research on regenerative braking control of electric vehicles based on game theory optimization. Science Progress, 107(2), 00368504241247404. https://doi.org/10.1177/00368504241247404

[34] Habib, A.A., et al. (2023). Lithium-ion battery management system for electric vehicles: constraints, challenges, and recommendations. Batteries, 9(3), 152. https://doi.org/10.3390/batteries9030152

[35] Hua, C.-C., S.-J. Kao, and Y.-H. Fang. Design and implementation of a regenerative braking system for electric bicycles with a DSP controller. in 2013 1st International Future Energy Electronics Conference (IFEEC). 2013. IEEE. https://doi.org/10.1109/IFEEC.2013.6687583

[36] Kesler, S., O. Boyaci, and M. Tumbek. (2022). Design and Implementation of a Regenerative Mode Electric Vehicle Test Platform for Engineering Education. Sustainability, 14(21), 14316. https://doi.org/10.3390/su142114316

[37] Hamada, A.T. and M.F. Orhan. (2022). An overview of regenerative braking systems. Journal of Energy Storage, 52, 105033. https://doi.org/10.1016/j.est.2022.105033

[38] Zhang, Y., H. Xie, and K. Song. An optimal vehicle speed planning algorithm for regenerative braking at traffic lights intersections based on reinforcement learning. in 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI). 2020. IEEE. https://doi.org/10.1109/CVCI51460.2020.9338590

[39] Pugi, L., et al. Application of regenerative braking on electric vehicles. in 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe). 2019. IEEE. https://doi.org/10.1109/EEEIC.2019.8783318

[40] Vasiljević, S., et al. Regenerative braking on electric vehicles: working principles and benefits of application. in IOP Conference Series: Materials Science and Engineering. 2022. IOP Publishing. https://doi.org/10.1088/1757-899X/1271/1/012025

[41] Yin, Z., et al. (2023). Regenerative braking of electric vehicles based on fuzzy control strategy. Processes, 11(10), 2985. https://doi.org/10.3390/pr11102985

[42] Shi, B., et al. (2023). A review of silicon carbide MOSFETs in electrified vehicles: Application, challenges, and future development. IET Power Electronics, 16(12), 2103-2120. https://doi.org/10.1049/pel2.12524

[43] Conte, F.V. (2006). Battery and battery management for hybrid electric vehicles: a review. e & i Elektrotechnik und Informationstechnik, 123(10), 424-431. https://doi.org/10.1007/s00502-006-0383-6

[44] Lin, X., et al. (2019). Modeling and estimation for advanced battery management. Annual Review of Control, Robotics, and Autonomous Systems, 2(1), 393-426. https://doi.org/10.1146/annurev-control-053018-023643

[45] Li, Z., et al. (2023). Research on Regenerative Braking Control Strategy for Single-Pedal Pure Electric Commercial Vehicles. World Electric Vehicle Journal, 14(8), 229. https://doi.org/10.3390/wevj14080229

[46] Rezk, M. and H. Abuzied. (2023). Artificial Neural Networks: A Promising Tool for Regenerative Braking Control in Electric Vehicles. European Journal of Engineering and Technology Research, 8(5), 49-58. https://doi.org/10.24018/ejeng.2023.8.5.3098.

[47] Saiteja, P., et al. (2022). Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs. International Journal of Energy Research, 46(14), 20146-20179. https://doi.org/10.1002/er.8306

[48] Alanazi, F. (2023). Electric vehicles: Benefits, challenges, and potential solutions for widespread adaptation. Applied Sciences, 13(10), 6016. https://doi.org/10.3390/app13106016

[49] Islameka, M., et al., Energy management systems for battery electric vehicles, in Emerging Trends in Energy Storage Systems and Industrial Applications. 2023, Elsevier. p. 113-150. https://doi.org/10.1016/B978-0-323-90521-3.00006-5

[50] Hwang, M.H., et al. (2023). Regenerative braking control strategy based on AI algorithm to improve driving comfort of autonomous vehicles. Applied Sciences, 13(2), 946. https://doi.org/10.3390/app13020946

[51] Chen, W.-C., W.-H. Chen, and S.-Y. Yang. An intelligent multi-agent system of mobile information consultation and sharing with big data and time series analysis technologies for smart tourism. in 2018 IEEE International Conference on Applied System Invention (ICASI). 2018. IEEE. https://doi.org/10.1109/ICASI.2018.8394253

[52] Yin, L., et al. (2022). Designing wearable microgrids: towards autonomous sustainable on-body energy management. Energy & Environmental Science, 15(1), 82-101. https://doi.org/10.1039/D1EE03113A

[53] Qiu, C., et al. (2018). A novel control strategy of regenerative braking system for electric vehicles under safety critical driving situations. Energy, 149, 329-340. https://doi.org/10.1016/j.energy.2018.02.046

[54] Maia, R., et al. (2020). Regenerative braking system modeling by fuzzy Q-Learning. Engineering Applications of Artificial Intelligence, 93, 103712. https://doi.org/10.1016/j.engappai.2020.103712

[55] Yin, Y., et al. (2024). DQN regenerative braking control strategy based on adaptive weight coefficients. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 238(10-11), 2956-2966. https://doi.org/10.1177/09544070231186200

[56] Cheng, H., et al. (2020). An integrated SRM powertrain topology for plug-in hybrid electric vehicles with multiple driving and onboard charging capabilities. IEEE Transactions on Transportation Electrification, 6(2), 578-591. https://doi.org/10.1109/TTE.2020.2987167

[57] Anh, N.T., C.-K. Chen, and X. Liu. (2024). An Efficient Regenerative Braking System for Electric Vehicles Based on a Fuzzy Control Strategy. Vehicles, 6(3), 1496-1512. https://doi.org/10.3390/vehicles6030071

[58] Yan, N., et al. (2021). Research on energy management and control method of microgrid considering health status of batteries in echelon utilization. Energy Reports, 7, 389-395. https://doi.org/10.1016/j.egyr.2021.01.055

[59] Mehlig, D., et al. (2021). Electrification of Road Transport and the Impacts on Air Quality and Health in the UK. Atmosphere, 12(11), 1491. https://doi.org/10.3390/atmos12111491

[60] Jafari, M., et al. (2021). Stochastic synergies of urban transportation system and smart grid in smart cities considering V2G and V2S concepts. Energy, 215, 119054. https://doi.org/10.1016/j.energy.2020.119054

[61] Chengula, T.J., et al. (2024). Enhancing advanced driver assistance systems through explainable artificial intelligence for driver anomaly detection. Machine Learning with Applications, 17, 100580. https://doi.org/10.1016/j.mlwa.2024.100580

[62] Zhu, Y., H. Wu, and C. Zhen. (2021). Regenerative braking control under sliding braking condition of electric vehicles with switched reluctance motor drive system. Energy, 230, 120901. https://doi.org/10.1016/j.energy.2021.120901

[63] Zhang, L. and X. Cai. (2018). Control strategy of regenerative braking system in electric vehicles. Energy Procedia, 152, 496-501. https://doi.org/10.1016/j.egypro.2018.09.200

[64] Khodaparastan, M., A.A. Mohamed, and C. Spanos. (2022). Regenerative Braking Energy in Electric Railway Systems. Transportation Electrification: Breakthroughs in Electrified Vehicles, Aircraft, Rolling Stock, and Watercraft, 343-366. https://doi.org/10.1002/9781119812357.ch15

[65] Das, L.C. and M. Won. Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. in International Conference on Machine Learning. 2021. PMLR. https://proceedings.mlr.press/v139/das21a.html

[66] Shi, R., et al. (2020). Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization. Renewable Energy, 153, 1067-1080. https://doi.org/10.1016/j.renene.2020.02.027

[67] Brosowsky, M., et al. Safe deep reinforcement learning for adaptive cruise control by imposing state-specific safe sets. in 2021 IEEE Intelligent Vehicles Symposium (IV). 2021. IEEE. https://doi.org/10.1109/IV48863.2021.9575258

[68] Budijono, A.P., I.N. Sutantra, and A.S. Pramono. (2023). Optimizing regenerative braking on electric vehicles using a model-based algorithm in the antilock braking system. International Journal of Power Electronics and Drive Systems, 14(1), 131. https://doi.org/10.11591/ijpeds.v14.i1.pp131-139

[69] Qi, Z., et al. (2022). Learning-based path planning and predictive control for autonomous vehicles with low-cost positioning. IEEE Transactions on Intelligent Vehicles, 8(2), 1093-1104. https://doi.org/10.1109/TIV.2022.3146972

[70] Sonny, A., S.R. Yeduri, and L.R. Cenkeramaddi. (2023). Q-learning-based unmanned aerial vehicle path planning with dynamic obstacle avoidance. Applied Soft Computing, 147, 110773. https://doi.org/10.1016/j.asoc.2023.110773

[71] Güney, B. and H. Kiliç. (2020). Research on Regenerative Braking Systems: A Review. International Journal of Science and Research (IJSR), 9(9), 160-166. http://dx.doi.org/10.21275/SR20902143703

[72] Phuthong, T., et al. (2024). Identifying factors influencing electric vehicle adoption in an emerging market: The case of Thailand. Transportation Research Interdisciplinary Perspectives, 27, 101229. https://doi.org/10.1016/j.trip.2024.101229

[73] Jamadar, N.M. and H.T. Jadhav. (2021). A review on braking control and optimization techniques for electric vehicle. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 235(9), 2371-2382. https://doi.org/10.1177/0954407021996906

[74] Oladimeji, D., et al. (2023). Smart transportation: an overview of technologies and applications. Sensors, 23(8), 3880. https://doi.org/10.3390/s23083880

[75] Mhatre, A.S. and P. Shukla. (2024). A comprehensive review of energy harvesting technologies for sustainable electric vehicles. Environmental Science and Pollution Research, 1-24. https://doi.org/10.1007/s11356-024-34865-8

[76] Abdulhai, B., R. Pringle, and G.J. Karakoulas. (2003). Reinforcement learning for true adaptive traffic signal control. Journal of Transportation Engineering, 129(3), 278-285. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:3(278)

[77] Zhu, C. (2023). An adaptive agent decision model based on deep reinforcement learning and autonomous learning. J. Logist. Inform. Serv. Sci, 10(3), 107-118. https://doi.org/10.33168/JLISS.2023.0309

[78] Dianin, A., E. Ravazzoli, and G. Hauger. (2021). Implications of autonomous vehicles for accessibility and transport equity: A framework based on literature. Sustainability, 13(8), 4448. https://doi.org/10.3390/su13084448

[79] Schwarting, W., J. Alonso-Mora, and D. Rus. (2018). Planning and decision-making for autonomous vehicles. Annual Review of Control, Robotics, and Autonomous Systems, 1(1), 187-210. https://doi.org/10.1146/annurev-control-060117-105157

[80] Mussi, M., et al. (2023). Arlo: A framework for automated reinforcement learning. Expert Systems with Applications, 224, 119883. https://doi.org/10.1016/j.eswa.2023.119883

[81] Li, C., et al. (2024). A review of reinforcement learning based hyper-heuristics. PeerJ Computer Science, 10, e2141. https://doi.org/10.7717/peerj-cs.2141

[82] Tubaishat, M., et al. (2009). Wireless sensor networks in intelligent transportation systems. Wireless communications and mobile computing, 9(3), 287-302. https://doi.org/10.1002/wcm.616

[83] Reda, M., et al. (2024). Path planning algorithms in the autonomous driving system: A comprehensive review. Robotics and Autonomous Systems, 174, 104630. https://doi.org/10.1016/j.robot.2024.104630

[84] Angeloni, D., et al. A RISC-V Hardware Accelerator for Q-Learning Algorithm. in International Conference on Applications in Electronics Pervading Industry, Environment and Society. 2023. Springer. https://doi.org/10.1007/978-3-031-48121-5_11

[85] Choi, D.D. and P.B. Lowry. (2024). Balancing the commitment to the common good and the protection of personal privacy: Consumer adoption of sustainable, smart connected cars. Information & management, 61(1), 103876. https://doi.org/10.1016/j.im.2023.103876

[86] Tariq, U., et al. (2023). A critical cybersecurity analysis and future research directions for the internet of things: A comprehensive review. Sensors, 23(8), 4117. https://doi.org/10.3390/s23084117

[87] Singh, B. and A. Gupta. (2015). Recent trends in intelligent transportation systems: a review. Journal of transport literature, 9(2), 30-34. https://doi.org/10.1590/2238-1031.jtl.v9n2a6

[88] Carton, F., Exploration of reinforcement learning algorithms for autonomous vehicle visual perception and control. 2021, Institut Polytechnique de Paris. https://theses.hal.science/tel-03273748/

[89] El Chamie, M., D. Janak, and B. Açıkmeşe. (2019). Markov decision processes with sequential sensor measurements. Automatica, 103, 450-460. https://doi.org/10.1016/j.automatica.2019.02.026

[90] Oroumiyeh, F. and Y. Zhu. (2021). Brake and tire particles measured from on-road vehicles: Effects of vehicle mass and braking intensity. Atmospheric Environment: X, 12, 100121. https://doi.org/10.1016/j.aeaoa.2021.100121

[91] Taylor, H. and M.D. Vestergaard. (2022). Developmental dyslexia: disorder or specialization in exploration? Frontiers in psychology, 13, 889245. https://doi.org/10.3389/fpsyg.2022.889245

[92] Khan, T., et al. (2024). A multi-criteria design framework for sustainable electric vehicles stations. Sustainable Cities and Society, 101, 105076. https://doi.org/10.1016/j.scs.2023.105076

[93] Akhade, P., et al. A Review on control strategies for integration of electric vehicles with power systems. in 2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D). 2018. IEEE. https://doi.org/10.1109/TDC.2018.8440139

[94] Abdel-Basset, M., R. Mohamed, and M. Abouhawwash. (2021). Balanced multi-objective optimization algorithm using improvement based reference points approach. Swarm and Evolutionary Computation, 60, 100791. https://doi.org/10.1016/j.swevo.2020.100791

[95] Tian, Y., et al. (2021). Balancing objective optimization and constraint satisfaction in constrained evolutionary multiobjective optimization. IEEE Transactions on Cybernetics, 52(9), 9559-9572. https://doi.org/10.1109/TCYB.2020.3021138

[96] Ning, X., et al. (2023). Regenerative braking algorithm for parallel hydraulic hybrid vehicles based on fuzzy Q-Learning. Energies, 16(4), 1895. https://doi.org/10.3390/en16041895

[97] Xu, B., et al. (2022). Hierarchical Q-learning network for online simultaneous optimization of energy efficiency and battery life of the battery/ultracapacitor electric vehicle. Journal of Energy Storage, 46, 103925. https://doi.org/10.1016/j.est.2021.103925

[98] Youssef, O.E., M.G. Hussien, and A. El-Wahab Hassan. (2024). A Robust Regenerative‐Braking Control of Induction Motors for EVs Applications. International Transactions on Electrical Energy Systems, 2024(1), 5526545. https://doi.org/10.1155/2024/5526545

[99] Mepparambath, R.M., L. Cheah, and C. Courcoubetis. (2021). A theoretical framework to evaluate the traffic impact of urban freight consolidation centres. Transportation Research Part E: Logistics and Transportation Review, 145, 102134. https://doi.org/10.1016/j.tre.2020.102134

[100] Weiwei, Y., L. Denghao, and Z. Wenming. (2024). Regenerative Braking Strategy Based on Deep Reinforcement Learning for an Electric Mining Truck. Chinese Journal of Engineering, 46(3), 503-513. https://dx.doi.org/10.13374/j.issn2095-9389.2023.06.01.003

[101] Zheng, H., et al. (2023). Learning-based safe control for robot and autonomous vehicle using efficient safety certificate. IEEE Open Journal of Intelligent Transportation Systems, 4, 419-430. https://doi.org/10.1109/OJITS.2023.3280573

[102] Li, G., et al. (2021). Risk assessment based collision avoidance decision-making for autonomous vehicles in multi-scenarios. Transportation research part C: emerging technologies, 122, 102820. https://doi.org/10.1016/j.trc.2020.102820

[103] Veerabathraswamy, S. and N. Bhatt. Safe q-learning approaches for human-in-loop reinforcement learning. in 2023 Ninth Indian Control Conference (ICC). 2023. IEEE. https://doi.org/10.1109/ICC61519.2023.10442899

[104] Wei, Z., J. Xu, and D. Halim. (2017). Braking force control strategy for electric vehicles with load variation and wheel slip considerations. IET Electrical Systems in Transportation, 7(1), 41-47. https://doi.org/10.1049/iet-est.2016.0023

[105] Khan, N. and Shreasth. A Novel Active Cell Balancing Approach Based on Reinforcement Learning for SoC Balancing of Four Lithium-Ion Battery Cells. in International Conference on Electrical, Control & Computer Engineering. 2023. Springer. https://doi.org/10.1007/978-981-97-3847-2_46

[106] Alqahtani, H. and G. Kumar. (2024). Machine learning for enhancing transportation security: A comprehensive analysis of electric and flying vehicle systems. Engineering Applications of Artificial Intelligence, 129, 107667. https://doi.org/10.1016/j.engappai.2023.107667