Method for modeling battery depreciation using a depreciation density function and control method for a battery depreciation model

Modeling battery depreciation density by SoC using second-order polynomial curve fitting of average depreciation

This technology models battery depreciation by calculating the Average Wear Cost (AWC) from Accumulated Cycle Count (ACC) data and applying second-order polynomial curve fitting to derive a depreciation density function that varies according to the State of Charge (SoC).

Previously, power functions were used to fit cycle life data, but significant discrepancies with actual measurements led to overfitting. This error hindered the accurate calculation of battery operating costs and the development of effective charge/discharge schedules.

Instead of fitting the cycle life function directly, this technology calculates the AWC based on total usage per Depth of Discharge (DoD) and battery price, then applies second-order polynomial curve fitting to minimize errors. The resulting SoC-based depreciation density function is then integrated into power grid frequency regulation control. This enables highly profitable operational strategies that account for battery degradation costs, applicable to grid-scale ESS operations, frequency regulation ancillary service bidding, and EV V2G scheduling.

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Key Features:

• Calculating the Average Wear Cost (AWC) from battery price using the Accumulated Cycle Count (ACC) and total usage based on the Depth of Discharge (DoD).
• Deriving a depreciation density function for each State of Charge (SoC) by applying second-order polynomial curve fitting to the calculated AWC with respect to the Depth of Discharge.
• Receiving status information for each battery in the power grid and calculating battery depreciation using the depreciation density function.
• Performing frequency regulation to maintain stable grid frequency by accounting for the calculated depreciation when managing supply and demand fluctuations.

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