System Models#

A battery system, regardless of its scale or technology, is represented by…

  • A transient state representing how the parameters which vary predictably given operating conditions, like the state of charge

  • An advanced state of health (ASOH) representing the parameters which vary slowly and unpredictably, like the internal resistance

  • A set of input quantities and output quantities that define how a system is used and what signals it produces.

  • A cell model describing how the transient state evolves over time given the ASOH and inputs.

  • A degradation model which captures how ASOH parameters are expected to change with time and use.

Transient State, Inputs, and Outputs#

Transient states, inputs, and outputs are represented as a GeneralContainer object specific to a certain type of battery.

The GeneralContainer stores numeric values corresponding to different states and can be easily transformed to or from NumPy arrays.

from moirae.base import GeneralContainer, ScalarParameter
import numpy as np

class SimpleTransientState(GeneralContainer):

    soc: ScalarParameter = 0.
    """How much the battery has been charged"""

state = SimpleTransientState(soc=1.)

# Parameters are stored as a 2D arrays where the
#  first dimension is a batch dimension
assert state.soc == np.array([[1.]])
assert state.to_numpy() == np.array([[1.]])

All inputs must include the terminal current, and all outputs must include the time terminal voltage. Beyond that, implementations can include any and all parameters necessary to specify a particular storage systems.

Note

The InputQuantities and OutputQuantities classes define the required names for the time, current, and voltage.

Note

Moirae’s models assume that a positive current corresponds to charging a battery, opposite from the battery-data-toolkit’s choice, but in line with conventions common for battery modeling.

Health Parameters#

The health parameters for a battery are defined as a subclass of HealthVariable. As with the transient states, health parameters are specific to the type of battery. Unlike transient states, the way they are defined allows a hierarchical structure and the ability annotate which parameters are treated as updatable.

Consider the following parameter set as an example

class Resistance(HealthVariable):
    full: ScalarParameter
    '''Resistance at fully charged'''
    empty: ScalarParameter
    '''Resistance at fully discharged'''

    def get_resistance(self, soc: float):
        return self.empty + soc * (self.full - self.empty)

class BatteryHealth(HealthVariable):
    capacity: ScalarParameter
    resistance: Resistance

model = BatteryHealth(capacity=1., resistance={'full': 0.2, 'empty': 0.1})

Accessing Values#

All variables are stored as 2D arrays, regardless of whether they are scalar values (like the theoretical capacity) or vectors (like the open circuit voltage at different charge states).

Access value of a parameter from the Python attributes

assert np.allclose(model.resistance.full, [[0.2]])  # Attribute is 2D with shape (1, 1)

or indirectly using get_parameters().

assert np.allclose(model.get_parameters(['resistance.full']), [[0.2]])

The name of a variable within a hierarchical health variable contains the path to its submodel and the name of the attribute of the submodel separated by periods. For example, the resistance at full charge is “resistance.full”.

Controlling Which Parameters Are Updatable#

No parameters of the HealthVariable are treated as updatable by default. As a result, no estimation scheme will alter their values.

Mark a variable as updatable by marking the submodel(s) holding that variable as updatable and the name of the variable to the updatable of its submodel. Marking “resistance.empty” is achieved by

model.updatable.add('resistance')
model.resistance.updatable.add('empty')

or using the mark_updatable() utility method

model.mark_updatable('resistance.empty')

All submodels along the path to a specific parameter must be updatable for it to be updatable. For example, “resistance.full” would not be considered updatable if the “resistance” submodel is not updatable

model.updatable.remove('resistance')
model.resistance.mark_updatable('full')  # Has no effect yet because 'resistance' is fixed

Setting Values of Parameters#

Provide a list of new values and a list of names to the update_parameters function.

model.updatable.add('resistance')  # Allows resistance fields to be updated
model.update_parameters([[0.1]], names=['resistance.full'])

or omit the specific names to set all updatable variables

assert model.updatable_names == ['resistance.full', 'resistance.empty']
model.update_parameters([[0.2, 0.1]])  # As a (1, 2) array for 1-sized batch of 2 values

Defining the Cell Physics#

All storage systems are represented using a CellModel that provides two functions:

  1. updating transient states, and

  2. predicting outputs (e.g., terminal voltage)

Cell models hold no state themselves and only implement the physics that describes how the state of a battery system should evolve with time. Attributes of a cell model adjust the how the calculations are performed or are resource-specific configuration, such as a path to external components.

Changes in the ASOH for a cell are described as DegradationModel. Such models provide a function which updates the current state of health provided new inputs, transient state, and measurements.

Available Cell Models#

Moirae already contains several cell models:

  • EquivalentCircuitModel: A Thevenin circuit model with no additional dependencies beyond those needed for Moirae.

  • TheveninModel: A Thevenin model which includes a simple thermal model and is built atop a robust ODE solver. Consult the documentation for Thevenin for installation instructions.

The “Extending Moirae” documentation explains how to add a new model. You need not contribute a new Cell Model to Moirae in order for it to work with the estimators but we would encourage you to.

On this page
Show Source