Add VolumetricEnergyDensity example

CHANGELOG.md

@@ -5,6 +5,7 @@
 - [#118](https://github.com/pybop-team/PyBOP/issues/118) - Adds example jupyter notebooks.
 - [#151](https://github.com/pybop-team/PyBOP/issues/151) - Adds a standalone version of the Problem class.
 - [#12](https://github.com/pybop-team/PyBOP/issues/12) - Adds initial implementation of an Observer class and an unscented Kalman filter.
+- [#190](https://github.com/pybop-team/PyBOP/issues/190) - Adds a second example design cost, namely the VolumetricEnergyDensity.
 
 ## Bug Fixes
 

examples/notebooks/spm_electrode_design.ipynb

@@ -13340,7 +13340,8 @@
     }
    ],
    "source": [
-    "cost.problem._model.approximate_capacity(x)\n",
+    "if cost.update_capacity:\n",
+    "    cost.problem._model.approximate_capacity(x)\n",
     "pybop.quick_plot(x, cost, title=\"Optimised Comparison\");"
    ]
   },

examples/scripts/spme_max_energy.py

@@ -1,7 +1,5 @@
 import pybop
 
-## NOTE: This is a brittle example, the classes and methods below will be
-## integrated into pybop in a future release.
 
 # A design optimisation example loosely based on work by L.D. Couto
 # available at https://doi.org/10.1016/j.energy.2022.125966.
@@ -12,6 +10,12 @@
 # electrode widths, particle radii, volume fractions and
 # separator width.
 
+# NOTE: This script can be easily adjusted to consider the volumetric
+# (instead of gravimetric) energy density by changing the line which
+# defines the cost and changing the output to:
+# print(f"Initial volumetric energy density: {-cost(cost.x0):.2f} Wh.m-3")
+# print(f"Optimised volumetric energy density: {-final_cost:.2f} Wh.m-3")
+
 # Define parameter set and model
 parameter_set = pybop.ParameterSet.pybamm("Chen2020")
 model = pybop.lithium_ion.SPMe(parameter_set=parameter_set)
@@ -42,7 +46,7 @@
     model, parameters, experiment, signal=signal, init_soc=init_soc
 )
 
-# Generate cost function and optimisation class
+# Generate cost function and optimisation class:
 cost = pybop.GravimetricEnergyDensity(problem)
 optim = pybop.Optimisation(
     cost, optimiser=pybop.PSO, verbose=True, allow_infeasible_solutions=False
@@ -56,7 +60,8 @@
 print(f"Optimised gravimetric energy density: {-final_cost:.2f} Wh.kg-1")
 
 # Plot the timeseries output
-# model.approximate_capacity(x)
+if cost.update_capacity:
+    cost.problem._model.approximate_capacity(x)
 pybop.quick_plot(x, cost, title="Optimised Comparison")
 
 # Plot the cost landscape with optimisation path

pybop/__init__.py

@@ -26,12 +26,16 @@
 #
 # Cost function class
 #
-from ._costs import (
-    BaseCost,
+from .costs.base_cost import BaseCost
+from .costs.fitting_costs import (
     RootMeanSquaredError,
     SumSquaredError,
     ObserverCost,
+)
+from .costs.design_costs import (
+    DesignCost,
     GravimetricEnergyDensity,
+    VolumetricEnergyDensity,
 )
 
 #