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BORNEO, ANGELO
Dipartimento Energia
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Battery Electric Vehicle Control Strategy for String Stability based on Deep Reinforcement Learning in V2V Driving
2023 Borneo, Angelo; Miretti, Federico; Acquarone, Matteo; Misul, Daniela
Acceleration control strategy for Battery Electric Vehicle based on Deep Reinforcement Learning in V2V driving
2022 Acquarone, Matteo; Borneo, Angelo; Misul, Daniela Anna
| Citazione | Data di pubblicazione | Autori | File |
|---|---|---|---|
| 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.pdf; Borneo et al_2023_Battery Electric Vehicle Control Strategy for String Stability based on Deep.pdf |
| 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.pdf; Acceleration_control_strategy_for_Battery_Electric_Vehicle_based_on_Deep_Reinforcement_Learning_in_V2V_driving_.pdf |