Abstract: Systems and methods for estimating battery degradation of a battery energy storage system (BESS) are disclosed. An iterative process is executed over a pre-defined time period divided into iterations. For each iteration, an average temperature of the BESS is determined by inputting a state of health (SOH) and charge rate into an average temperature look-up-table (LUT). The SOH for the next iteration is determined by inputting the determined average temperature into a set of cell degradation equations. The charge rate for the next iteration is derived from a usage profile which defines the charging and discharging cycles over the pre-defined time period and includes power and SOC over the pre-defined time period. The SOH of the BESS over the pre-defined time period may then be displayed on a user interface.

Abstract: Systems and methods for estimating battery degradation of a battery energy storage system (BESS) are disclosed. An iterative process is executed over a pre-defined time period divided into iterations. For each iteration, an average temperature of the BESS is determined by inputting a state of health (SOH) and charge rate into an average temperature look-up-table (LUT). The SOH for the next iteration is determined by inputting the determined average temperature into a set of cell degradation equations. The charge rate for the next iteration is derived from a usage profile which defines the charging and discharging cycles over the pre-defined time period and includes power and SOC over the pre-defined time period. The SOH of the BESS over the pre-defined time period may then be displayed on a user interface.

Abstract: Systems and methods for optimizing charging and discharging of a battery energy storage system (BESS) are disclosed. A pre-defined time period mapped to electricity price forecast data is partitioned into charging windows and discharging windows. A bidding profile may be generated by identifying combinations of charging windows and discharging windows that maximize a revenue value over the pre-defined time period. A power profile for the BESS derived from the bidding profile is generated, and a battery analytics profile derived from the power profile is generated. The battery analytics profile is assessed for compliance with convergence criteria. If the battery analytics profile complies with the convergence criteria, the bidding profile is transmitted to a server of an electricity market operator for approval, otherwise, the bidding profile is adjusted until the resulting battery analytics profile complies with the convergence criteria.

Abstract: Systems and methods for estimating battery degradation of a battery energy storage system (BESS) are disclosed. An iterative process is executed over a pre-defined time period divided into iterations. For each iteration, an average temperature of the BESS is determined by inputting a state of health (SOH) and charge rate into an average temperature look-up-table (LUT). The SOH for the next iteration is determined by inputting the determined average temperature into a set of cell degradation equations. The charge rate for the next iteration is derived from a usage profile which defines the charging and discharging cycles over the pre-defined time period and includes power and SOC over the pre-defined time period. The SOH of the BESS over the pre-defined time period may then be displayed on a user interface.