hydesign.battery_degradation

Classes

battery_degradation	Battery degradation model to predict the degradation of the battery throughout the lifetime of the plant
battery_loss_in_capacity_due_to_temp	Battery non-permanent loss of capacity due to low temp

Functions

incerase_resolution(ii_time, SoH, life, nn[, ...])
battery_replacement(rf_DoD, rf_SoC, rf_count, ...[, ...])	Battery degradation in steps and battery replacement
degradation(rf_DoD, rf_SoC, rf_count, rf_i_start, avr_tem)	Calculating the new level of capacity of the battery.
Linear_Degfun(rf_DoD, rf_SoC, rf_count, rf_i_start, ...)	Linear degradation function.
RFcount(SoC)
thermal_loss_of_storage(air_temp_C_t)	Battery temporary loss of storage at low temperatures. Simple piecewise linear fit from:

Module Contents

class hydesign.battery_degradation.battery_degradation(weather_fn, num_batteries=1, life_y=25, intervals_per_hour=1, weeks_per_season_per_year=None, battery_deg=True)

Bases: openmdao.api.ExplicitComponent

Battery degradation model to predict the degradation of the battery throughout the lifetime of the plant

Parameters:

• b_E_SOC_t (battery energy SOC time series)

• min_LoH (minimum level of health before death of battery)

Returns:

SoH

Return type:

battery state of health at discretization levels

life_h = 219000

life_intervals = 219000

yearly_intervals = 8760

num_batteries = 1

weather_fn

battery_deg = True

battery_rf_matrix = None

air_temp_K_t

setup()

Declare inputs and outputs.

Available attributes:

name pathname comm options

compute(inputs, outputs)

Compute outputs given inputs. The model is assumed to be in an unscaled state.

Parameters:

• inputs (Vector) – Unscaled, dimensional input variables read via inputs[key].

• outputs (Vector) – Unscaled, dimensional output variables read via outputs[key].

• discrete_inputs (dict or None) – If not None, dict containing discrete input values.

• discrete_outputs (dict or None) – If not None, dict containing discrete output values.

class hydesign.battery_degradation.battery_loss_in_capacity_due_to_temp(weather_fn, num_batteries=1, life_y=25, intervals_per_hour=1, weeks_per_season_per_year=None, battery_deg=True)

Bases: openmdao.api.ExplicitComponent

Battery non-permanent loss of capacity due to low temp

Parameters:

SoH (battery state of health at discretization levels)

Returns:

SoH_all

Return type:

battery state of health at discretization levels

life_h = 219000

yearly_intervals = 8760

life_intervals = 219000

num_batteries = 1

weather_fn

battery_deg = True

air_temp_C_t

setup()

Declare inputs and outputs.

Available attributes:

name pathname comm options

compute(inputs, outputs)

Compute outputs given inputs. The model is assumed to be in an unscaled state.

Parameters:

• inputs (Vector) – Unscaled, dimensional input variables read via inputs[key].

• outputs (Vector) – Unscaled, dimensional output variables read via outputs[key].

• discrete_inputs (dict or None) – If not None, dict containing discrete input values.

• discrete_outputs (dict or None) – If not None, dict containing discrete output values.

hydesign.battery_degradation.incerase_resolution(ii_time, SoH, life, nn, hourly_intervals=1)

hydesign.battery_degradation.battery_replacement(rf_DoD, rf_SoC, rf_count, rf_i_start, avr_tem, min_LoH, n_steps_in_LoH=30, num_batteries=2)

Battery degradation in steps and battery replacement

Parameters:

• rf_DoD (depth of discharge after rainflow counting)

• rf_SoC (mean SoC after rainflow counting)

• rf_count (half or full cycle after rainflow counting, ethier 0.5 or 1)

• rf_i_start (time index for the cycles [in hours])

• avr_tem (average temperature in the location, yearly or more long. default value is 20)

• min_LoH (minimum level of health before death of battery)

• n_steps_in_LoH (number of discretizations in battery state of health)

• num_batteries (number of battery replacements)

Returns:

• LoC (battery level of capacity)

• ind_q (time indices for constant health levels)

• ind_q_last (time index for battery replacement)

hydesign.battery_degradation.degradation(rf_DoD, rf_SoC, rf_count, rf_i_start, avr_tem, LLoC_0=0)

Calculating the new level of capacity of the battery.

Xu, B., Oudalov, A., Ulbig, A., Andersson, G., and Kirschen, D. S.: Modeling of lithium-ion battery degradation for cell life assessment, IEEE Transactions on Smart Grid, 9, 1131–1140, 2016.

Parameters:

• rf_DoD (depth of discharge after rainflow counting)

• rf_SoC (mean SoC after rainflow counting)

• rf_count (half or full cycle after rainflow counting, ethier 0.5 or 1)

• rf_i_start (time index for the cycles [in hours])

• avr_tem (average temperature in the location, yearly or more long. default value is 20)

Returns:

• LoC (battery level of capacity)

• LoC1

• LLoC

hydesign.battery_degradation.Linear_Degfun(rf_DoD, rf_SoC, rf_count, rf_i_start, avr_tem)

Linear degradation function.

Xu, B., Oudalov, A., Ulbig, A., Andersson, G., and Kirschen, D. S.: Modeling of lithium-ion battery degradation for cell life assessment, IEEE Transactions on Smart Grid, 9, 1131–1140, 2016.

Parameters:

• rf_DoD (depth of discharge after rainflow counting)

• rf_SoC (mean SoC after rainflow counting)

• rf_count (half or full cycle after rainflow counting, ethier 0.5 or 1)

• rf_i_start (time index for the cycles [in hours])

• avr_tem (average temperature in the location, yearly or more long. default value is 20)

Return type:

np.array(LLoC_hist)

hydesign.battery_degradation.RFcount(SoC)

hydesign.battery_degradation.thermal_loss_of_storage(air_temp_C_t)

Battery temporary loss of storage at low temperatures. Simple piecewise linear fit from:

Lv, S., Wang, X., Lu, W., Zhang, J., & Ni, H. (2021). The influence of temperature on the capacity of lithium ion batteries with different anodes. Energies, 15(1), 60.