Project and Development of a Reinforcement Learning Based Control Algorithm for Hybrid Electric Vehicles
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Cardoso, 2020, A review of micro and mild hybrid systems, Energy Rep., 6, 385, 10.1016/j.egyr.2019.08.077
Enang, 2017, Modelling and control of hybrid electric vehicles (A comprehensive review), Renew. Sustain. Energy Rev., 74, 1210, 10.1016/j.rser.2017.01.075
Rizzo, 2020, A survey on through-the-road hybrid electric vehicles, Electronics, 9, 1, 10.3390/electronics9050879
Singh, 2019, A comprehensive review on hybrid electric vehicles: Architectures and components, J. Mod. Transp., 27, 77, 10.1007/s40534-019-0184-3
Torreglosa, 2020, Analyzing the improvements of energy management systems for hybrid electric vehicles using a systematic literature review: How far are these controls from rule-based controls used in commercial vehicles?, Appl. Sci., 10, 1, 10.3390/app10238744
Martinez, 2017, Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective, IEEE Trans. Veh. Technol., 66, 4534, 10.1109/TVT.2016.2582721
Biswas, 2019, Energy management systems for electrified powertrains: State-of-the-art review and future trends, IEEE Trans. Veh. Technol., 68, 6453, 10.1109/TVT.2019.2914457
Hofman, T., van Druten, R.M., Steinbuch, M., and Serrarens, A.F.A. (2008). Rule-Based Equivalent Fuel Consumption Minimization Strategies for Hybrid Vehicles, IFAC. IFAC Proceedings Volumes 2008.
Finesso, 2016, Robust equivalent consumption-based controllers for a dual-mode diesel parallel HEV, Energy Convers. Manag., 127, 124, 10.1016/j.enconman.2016.08.021
Lin, 2003, Power management strategy for a parallel hybrid electric truck, IEEE Trans Control Syst Technol. IEEE Trans. Control Syst. Technol., 11, 839, 10.1109/TCST.2003.815606
Huang, 2017, Model predictive control power management strategies for HEVs: A review, J. Power Sources, 341, 91, 10.1016/j.jpowsour.2016.11.106
Harold, 2020, Powertrain Control for Hybrid-Electric Vehicles Using Supervised Machine Learning, Vehicles, 2, 267, 10.3390/vehicles2020015
Finesso, 2016, An unsupervised machine-learning technique for the definition of a rule-based control strategy in a complex HEV, SAE Int. J. Altern. Powertrains, 5, 308, 10.4271/2016-01-1243
Ganesh, 2022, A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution, Renew. Sustain. Energy Rev., 154, 111833, 10.1016/j.rser.2021.111833
Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press.
Zhu, Z., Liu, Y., and Canova, M. (2020, January 1–3). Energy Management of Hybrid Electric Vehicles via Deep Q-Networks. Proceedings of the 2020 American Control Conference (ACC), Denver, CO, USA.
Han, 2019, Energy management based on reinforcement learning with double deep Q-learning for a hybrid electric tracked vehicle, Appl. Energy, 254, 113708, 10.1016/j.apenergy.2019.113708
Li, 2019, Energy management for a power-split hybrid electric bus via deep reinforcement learning with terrain information, Appl. Energy, 255, 113762, 10.1016/j.apenergy.2019.113762
Liu, 2015, Reinforcement Learning of Adaptive Energy Management With Transition Probability for a Hybrid Electric Tracked Vehicle, IEEE Trans. Ind. Electron., 62, 7837, 10.1109/TIE.2015.2475419
Liu, 2017, Reinforcement Learning Optimized Look-Ahead Energy Management of a Parallel Hybrid Electric Vehicle, IEEE/ASME Trans. Mechatron., 22, 1497, 10.1109/TMECH.2017.2707338
Mittal, 2020, Optimization of Energy Management Strategy for Range-Extended Electric Vehicle Using Reinforcement Learning and Neural Network, SAE Tech. Pap., 2020, 1
Biswas, A., Anselma, P.G., and Emadi, A. (2019, January 19–21). Real-Time Optimal Energy Management of Electrified Powertrains with Reinforcement Learning. Proceedings of the 2019 IEEE Transportation Electrification Conference and Expo (ITEC), Detroit, MI, USA.
Xu, 2020, Parametric study on reinforcement learning optimized energy management strategy for a hybrid electric vehicle, Appl. Energy, 259, 114200, 10.1016/j.apenergy.2019.114200
Ehsani, M., Gao, Y., Longo, S., and Ebrahimi, K.M. (2018). Modern Electric Hybrid Electric and Fuel Cell Vehicles, CRC Press.
Sundström, O., and Guzzella, L. (2009, January 8–10). A generic dynamic programming Matlab function. Proceedings of the 2009 IEEE Control Applications, (CCA) & Intelligent Control, (ISIC), St. Petersburg, Russia.
Finesso, 2016, Cost-optimized design of a dual-mode diesel parallel hybrid electric vehicle for several driving missions and market scenarios, Appl. Energy, 177, 366, 10.1016/j.apenergy.2016.05.080
Finesso, R., Misul, D., Spessa, E., and Venditti, M. (2018). Optimal design of power-split HEVs based on total cost of ownership and CO2 emission minimization. Energies, 11.
Maino, 2021, Optimal mesh discretization of the dynamic programming for hybrid electric vehicles, Appl. Energy, 292, 116920, 10.1016/j.apenergy.2021.116920
Spaan, M.T.J. (2012). Partially Observable Markon Decision Processes. Reinforcement Learning. Adaptation, Learning, and Optimization, Springer.
Watkins, C.J.C.H. (1989). Learning from Delayed Rewards. [PhD Thesis, King’s College].
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W. (2016). OpenAI Gym. arXiv.
Van Rossum, G. (2020). The Python Lybrary Reference, Release 3.8.5, Python Software Foundation.
Finesso, R., Spessa, E., and Venditti, M. (2014). Optimization of the layout and control strategy for parallel through-the-road hybrid electric vehicles. SAE Tech. Pap., 1.
Anselma, P.G., Belingardi, G., Falai, A., Maino, C., Miretti, F., Misul, D., and Spessa, E. (2019, January 2–4). Comparing parallel hybrid electric vehicle powertrains for real-world driving. Proceedings of the 2019 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE), Turin, Italy.
Maino, 2021, A deep neural network based model for the prediction of hybrid electric vehicles carbon dioxide emissions, Energy AI, 5, 100073, 10.1016/j.egyai.2021.100073