BORNEO, ANGELO

BORNEO, ANGELO  

Dipartimento Energia  

091283  

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Citazione Data di pubblicazione Autori File
Acceleration control strategy for Battery Electric Vehicle based on Deep Reinforcement Learning in V2V driving / Acquarone, Matteo; Borneo, Angelo; Misul, Daniela Anna. - ELETTRONICO. - (2022), pp. 202-207. (Intervento presentato al convegno 2022 IEEE Transportation Electrification Conference and Expo, ITEC 2022 tenutosi a Anaheim, CA, USA nel 15-17 June 2022) [10.1109/ITEC53557.2022.9813785]. 1-gen-2022 Acquarone, MatteoBorneo, AngeloMisul, Daniela Anna Acceleration_control_strategy_for_Battery_Electric_Vehicle_based_on_Deep_Reinforcement_Learning_in_V2V_driving.pdfAcceleration_control_strategy_for_Battery_Electric_Vehicle_based_on_Deep_Reinforcement_Learning_in_V2V_driving_.pdf
Platooning Cooperative Adaptive Cruise Control for Dynamic Performance and Energy Saving: A Comparative Study of Linear Quadratic and Reinforcement Learning-Based Controllers / Borneo, Angelo; Zerbato, Luca; Miretti, Federico; Tota, Antonio; Galvagno, Enrico; Misul, Daniela Anna. - In: APPLIED SCIENCES. - ISSN 2076-3417. - ELETTRONICO. - 13:18(2023). [10.3390/app131810459] 1-gen-2023 Borneo, AngeloZerbato, LucaMiretti, FedericoTota, AntonioGalvagno, EnricoMisul, Daniela Anna applsci-13-10459.pdf
Battery Electric Vehicle Control Strategy for String Stability based on Deep Reinforcement Learning in V2V Driving / Borneo, Angelo; Miretti, Federico; Acquarone, Matteo; Misul, Daniela. - In: SAE TECHNICAL PAPER. - ISSN 0148-7191. - ELETTRONICO. - (2023), pp. 1-7. (Intervento presentato al convegno 16th International Conference on Engines & Vehicles tenutosi a Capri, Italy nel September 10th - 14th, 2023) [10.4271/2023-24-0173]. 1-gen-2023 Borneo, AngeloMiretti, FedericoAcquarone, MatteoMisul, Daniela BEV_CACC_RL.pdfBorneo et al_2023_Battery Electric Vehicle Control Strategy for String Stability based on Deep.pdf