Online Estimators

The OnlineEstimator defines the interface for all online estimators. The Estimator operates using at least one Filter, which each rely on a Model to estimate how parameters evolve with time.

Building an Estimator

The online estimator is composed of one or more filters which estimate the values of different parts of the battery state in tandem. The framework in which the filters interact is defined by the choice of OnlineEstimator, which includes the JointEstimator.

Build an estimator by first constructing a BaseCellWrapper that defines how to update or estimate the measurements of a system for each subset of variables being estimated. Each estimator how the they interact with the underlying CellModel and DegradationModel. Moirae provides a library of wrappers with a consistent interface so that all estimators can work with any Filter or Model. For example, the JointEstimator requires the JointCellModelWrapper.

cell_function = JointCellModelWrapper(
  cell_model=ecm,
  asoh=rint_asoh,
  transients=rint_transient,
  input_template=rint_inputs,
  asoh_inputs=('r0.base_values',),
)

Build the filters that will update the estimate of parameters next. Every type of filter requires the model wrapper and initial estimates for the values of parameters. The initial estimates for parameters and the inputs to the system are defined as distributions, which are created from NumPy arrays of parameters. The Unscented Kálmán Filter is a common choice:

ukf = UKF(
  model=cell_function,
  initial_hidden=MultivariateGaussian(
    mean=np.array([0., 0., 0.05]),  # Three parameters: SOC, hysteresis, R0
    covariance=np.diag([0.01, 0.01, 0.01])
  ),
  initial_controls=MultivariateGaussian(
    mean=np.array([0., 1., 25.]),  # Three inputs: Time, Current, Temperature
    covariance=np.diag([0.001, 0.001, 0.5])
  )
)

Assemble the filters together to form the estimator as the last step.

ukf_joint = JointEstimator(joint_filter=ukf)

Estimators provide class methods that assemble common patterns of wrapper and filters in a single step. Read the documentation on each filter type for further details.

• Joint Online Estimation
  • Assembling Filters
• Dual Estimation
  • Assembling Filters
• Advanced: Converting Coordinate Systems
  • Using a Coordinate Converter
  • Selecting a Conversion Operator

Using an Estimator

Use the estimator by calling the step function to update the estimated state provided a new observation of the outputs of the system.

The step function returns a probability distribution of the expected state and expected outputs.

# Generate inputs and expected outputs
next_inputs = ECMInput(time=1., current=1.)
expected_transients = ECMTransientVector.provide_template(has_C0=False, num_RC=0)
next_outputs = ecm.calculate_terminal_voltage(next_inputs, expected_transients, rint_asoh)

# Step the estimator
state_dist, output_dist = ukf_joint.step(
  next_inputs,
  next_outputs
)

All estimators provide access to the state through the estimator.state attribute, which can include elements from the transient and ASOH.

Retrieve the identities of each state variable using estimator.state_names or access the current estimates for the transient state and ASOH via the get_estimated_state method.