| Naslov: | Evaluation of artificial intelligence-enhanced pid tuning for virtual thermal system calibration |
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| Avtorji: | ID Skobir, Tilen (Avtor)
ID Ramšak, Matjaž (Mentor) Več o mentorju... 
ID Semenič, Tilen (Komentor) |
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| Datoteke: |  MAG_Skobir_Tilen_2026.pdf (2,53 MB, Vsebina bo dosegljiva po 15.09.2029)
MD5: BC15D44F602EAA14A469C629CED811C9
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Magistrsko delo/naloga |
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| Tipologija: | 2.09 - Magistrsko delo |
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| Organizacija: | FS - Fakulteta za strojništvo
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| Opis: | This master's thesis presents the development and evaluation of an artificial intelligence methodology for autonomous PID controller calibration in a multi-loop vehicle thermal management system (VTMS). A co-simulation environment was constructed linking an electrified vehicle thermal model, a software control model, and a reinforcement learning agent with TD3 algorithm. The agent autonomously searched for optimal values of four PI gains for both the electronic expansion valve (EXV) and the compressor. The reward function was developed iteratively through four configurations. Compared to the initial engineer's gain approximation, the best episode reduced the mean absolute error (MAE) by 54%. The results confirm that a properly designed RL agent can autonomously find stable multi-loop PID gains, and that constructing a reliable co-simulation environment represents the primary technical bottleneck of the workflow. |
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| Ključne besede: | reinforcement learning, PID control, vehicle thermal management, electrified vehicle, co-simulation, FMU, TD3, reward shaping |
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| Kraj izida: | Maribor |
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| Leto izida: | 2026 |
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| PID: | 20.500.12556/DKUM-99588 |
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| Datum objave v DKUM: | 18.09.2026 |
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| Število ogledov: | 61 |
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| Število prenosov: | 0 |
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| Metapodatki: |    |
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| Področja: | KTFMB - FS
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