Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Состояние здоровья× | Состояние заряда× | |
|---|---|---|
| Область | Термодинамика | Термодинамика |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2017 | 2004 |
| Автор метода≠ | Craig Birkl | Gregory Plett |
| Тип≠ | Degradation assessment | Estimation algorithm |
| Основополагающий источник≠ | Birkl, C. R., Roberts, M. R., McTurk, E., Bruce, P. G., & Howey, D. A. (2017). Degradation diagnostics for lithium ion cells. Journal of Power Sources, 341, 373-386. 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 ↗ |
| Другие названия | SOH, health estimation | SOC, charge estimation |
| Связанные | 3 | 3 |
| Сводка≠ | State of Health (SOH) quantifies battery degradation by measuring how much capacity and power capability have been lost due to aging. SOH is expressed as a percentage (100% = new, 80% = end of life for many applications). Tracking SOH enables predictive maintenance, end-of-life detection, and accurate range/power predictions in aging systems. SOH reflects cumulative effects of cycling, calendar aging, and operating conditions. | 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. |
| ScholarGateНабор данных ↗ |
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