Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Urmărirea Punctului de Putere Maximă× | Starea de Încărcare× | |
|---|---|---|
| Domeniu | Termodinamică | Termodinamică |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 2007 | 2004 |
| Autorul original≠ | Trishan Esram | Gregory Plett |
| Tip≠ | Control algorithm | Estimation algorithm |
| Sursa seminală≠ | Villalva, M. G., Gazoli, J. R., & Ruppert Filho, E. (2009). Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Transactions on Power Electronics, 24(5), 1198-1208. DOI ↗ | Plett, G. L. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Journal of Power Sources, 134(2), 252-261. DOI ↗ |
| Denumiri alternative | MPPT, impedance matching | SOC, charge estimation |
| Înrudite | 3 | 3 |
| Rezumat≠ | Maximum Power Point Tracking (MPPT) is a control algorithm for photovoltaic and wind energy systems that continuously adjusts the electrical load to extract maximum power regardless of changing irradiance and temperature. Without MPPT, a solar panel or wind turbine operates below its power potential due to impedance mismatch with the load. MPPT boosts the annual energy yield by 15-25% depending on system and climate. | State of Charge (SOC) is the amount of energy available in a battery or energy storage system, expressed as a percentage of its maximum capacity. Accurate SOC estimation is critical for safe operation: underestimating SOC can cause unsafe discharges, overestimating can cause overcharging. SOC estimation combines current integration (coulomb counting), voltage-based methods, and Kalman filtering to achieve accuracy despite measurement noise and model uncertainties. |
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