Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Baterijas ekvivalentā elektriskā shēmas modelis× | Uzlādes stāvoklis× | |
|---|---|---|
| Nozare | Termodinamika | Termodinamika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads | 2004 | 2004 |
| Autors | Gregory Plett | Gregory Plett |
| Tips≠ | Battery simulation model | Estimation algorithm |
| Pirmavots≠ | Seaman, C. V., Strutt, A. S., & Murray, A. (2014). Portable and plug-in hybrid electric vehicle battery electric range impacts on U.S. gasoline consumption. Journal of Power Sources, 243, 773-783. link ↗ | 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 ↗ |
| Citi nosaukumi≠ | ECM, circuit model, battery model | SOC, charge estimation |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | The Battery Equivalent Circuit Model (ECM) represents battery electrochemical behavior using an electrical circuit analogy. It includes an ideal voltage source (open-circuit voltage dependent on state of charge), internal resistance(s) for ohmic losses, and capacitive/resistive elements for transient response. ECM enables rapid simulation of battery behavior in electric vehicles, renewable energy systems, and portable devices without solving complex electrochemical equations. | 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. |
| ScholarGateDatu kopa ↗ |
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