# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module defines the abstract base classes for battery-related classes.
Regardless of the kind of electrode, conversion or insertion, there are many
common definitions and properties, e.g., average voltage, capacity, etc. which
can be defined in a general way. The Abc for battery classes implements some of
these common definitions to allow sharing of common logic between them.
"""
__author__ = "Anubhav Jain, Shyue Ping Ong"
__copyright__ = "Copyright 2012, The Materials Project"
__version__ = "0.1"
__maintainer__ = "Shyue Ping Ong"
__email__ = "shyuep@gmail.com"
__date__ = "Feb 1, 2012"
__status__ = "Beta"
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Dict, Tuple
from monty.json import MSONable
from scipy.constants import N_A
from pymatgen.core import Composition, Element
from pymatgen.entries.computed_entries import ComputedEntry
[docs]@dataclass
class AbstractVoltagePair(MSONable):
"""
An Abstract Base Class for a Voltage Pair.
Attributes:
voltage : Voltage of voltage pair.
mAh: Energy in mAh.
mass_charge: Mass of charged pair.
mass_discharge: Mass of discharged pair.
vol_charge: Vol of charged pair.
vol_discharge: Vol of discharged pair.
frac_charge: Frac of working ion in charged pair.
frac_discharge: Frac of working ion in discharged pair.
working_ion_entry: Working ion as an entry.
framework : The compositions of one formula unit of the host material
"""
voltage: float
mAh: float
mass_charge: float
mass_discharge: float
vol_charge: float
vol_discharge: float
frac_charge: float
frac_discharge: float
working_ion_entry: ComputedEntry
_framework_formula: str # should be made into Composition whenever the as_dict and from dict are fixed
def __post_init__(self):
# ensure the the frame work is a reduced composition
self._framework_formula = self.framework.reduced_formula
@property
def working_ion(self) -> Element:
"""
working ion as pymatgen Element object
"""
return self.working_ion_entry.composition.elements[0]
@property
def framework(self) -> Composition:
"""
The composition object representing the framework
"""
return Composition(self._framework_formula)
@property
def x_charge(self) -> float:
"""
The number of working ions per formula unit of host in the charged state
"""
return self.frac_charge * self.framework.num_atoms / (1 - self.frac_charge)
@property
def x_discharge(self) -> float:
"""
The number of working ions per formula unit of host in the discharged state
"""
return self.frac_discharge * self.framework.num_atoms / (1 - self.frac_discharge)
[docs]@dataclass
class AbstractElectrode(Sequence, MSONable):
"""
An Abstract Base Class representing an Electrode. It is essentially a
sequence of VoltagePairs. Generally, subclasses only need to implement
three abstract properties: voltage_pairs, working_ion and
working_ion_entry.
The general concept is that all other battery properties such as capacity,
etc. are derived from voltage pairs.
One of the major challenges with representing battery materials is keeping
track of the normalization between different entries. For example, one
entry might be TiO2 with one unit cell whereas another is LiTi2O4 with two
unit cells. When computing battery properties, it is needed to always use
a universal reference state otherwise you have normalization errors (e.g.,
the energy of LiTi2O4 must be divided by two to be compared with TiO2).
For properties such as volume, mass, or mAh transferred within the voltage
pair, a universal convention is necessary. AbstractElectrode can query for
extrinsic properties of several different AbstractVoltagePairs belonging to
a single charge/discharge path and be confident that the normalization is
being carried out properly throughout, even if more AbstractVoltagePairs
are added later.
The universal normalization is defined by the reduced structural framework
of the entries, which is common along the entire charge/discharge path. For
example, LiTi2O4 has a reduced structural framework of TiO2. Another
example is Li9V6P16O58 which would have a reduced structural framework of
V3P8O29. Note that reduced structural frameworks need not be
charge-balanced or physical, e.g. V3P8O29 is not charge-balanced, they are
just a tool for normalization.
Example: for a LiTi2O4 -> TiO2 AbstractVoltagePair, extrinsic quantities
like mAh or cell volumes are given per TiO2 formula unit.
Developers implementing a new battery (other than the two general ones
already implemented) need to implement a VoltagePair and an Electrode.
Attributes:
voltage_pairs: Objects that represent each voltage step
working_ion: Representation of the working ion that only contains element type
working_ion_entry: Representation of the working_ion that contains the energy
framework: The compositions of one formula unit of the host material
"""
voltage_pairs: Tuple[AbstractVoltagePair]
working_ion_entry: ComputedEntry
_framework_formula: str # should be made into Composition whenever the as_dict and from dict are fixed
def __post_init__(self):
# ensure the the frame work is a reduced composition
self._framework_formula = self.framework.reduced_formula
def __getitem__(self, index):
return self.voltage_pairs[index]
def __contains__(self, obj):
return obj in self.voltage_pairs
def __iter__(self):
return self.voltage_pairs.__iter__()
def __len__(self):
return len(self.voltage_pairs)
@property
def working_ion(self):
"""
working ion as pymatgen Element object
"""
return self.working_ion_entry.composition.elements[0]
@property
def framework(self):
"""
The composition object representing the framework
"""
return Composition(self._framework_formula)
@property
def x_charge(self) -> float:
"""
The number of working ions per formula unit of host in the charged state
"""
return self.voltage_pairs[0].x_charge
@property
def x_discharge(self) -> float:
"""
The number of working ions per formula unit of host in the discharged state
"""
return self.voltage_pairs[-1].x_discharge
@property
def max_delta_volume(self):
"""
Maximum volume change along insertion
"""
vols = [v.vol_charge for v in self.voltage_pairs]
vols.extend([v.vol_discharge for v in self.voltage_pairs])
return max(vols) / min(vols) - 1
@property
def num_steps(self):
"""
The number of distinct voltage steps in from fully charge to discharge
based on the stable intermediate states
"""
return len(self.voltage_pairs)
@property
def max_voltage(self):
"""
Highest voltage along insertion
"""
return max([p.voltage for p in self.voltage_pairs])
@property
def min_voltage(self):
"""
Lowest voltage along insertion
"""
return min([p.voltage for p in self.voltage_pairs])
@property
def max_voltage_step(self):
"""
Maximum absolute difference in adjacent voltage steps
"""
steps = [
self.voltage_pairs[i].voltage - self.voltage_pairs[i + 1].voltage
for i in range(len(self.voltage_pairs) - 1)
]
return max(steps) if len(steps) > 0 else 0
@property
def normalization_mass(self):
"""
Returns: Mass used for normalization. This is the mass of the discharged
electrode of the last voltage pair.
"""
return self.voltage_pairs[-1].mass_discharge
@property
def normalization_volume(self):
"""
Returns: Mass used for normalization. This is the vol of the discharged
electrode of the last voltage pair.
"""
return self.voltage_pairs[-1].vol_discharge
[docs] def get_sub_electrodes(self, adjacent_only=True):
"""
If this electrode contains multiple voltage steps, then it is possible
to use only a subset of the voltage steps to define other electrodes.
Must be implemented for each electrode object.
Args:
adjacent_only: Only return electrodes from compounds that are
adjacent on the convex hull, i.e. no electrodes returned
will have multiple voltage steps if this is set true
Returns:
A list of Electrode objects
"""
NotImplementedError(
"The get_sub_electrodes function must be implemented for each concrete electrode "
f"class {self.__class__.__name__,}"
)
[docs] def get_average_voltage(self, min_voltage=None, max_voltage=None):
"""
Average voltage for path satisfying between a min and max voltage.
Args:
min_voltage (float): The minimum allowable voltage for a given
step.
max_voltage (float): The maximum allowable voltage allowable for a
given step.
Returns:
Average voltage in V across the insertion path (a subset of the
path can be chosen by the optional arguments)
"""
pairs_in_range = self._select_in_voltage_range(min_voltage, max_voltage)
if len(pairs_in_range) == 0:
return 0
total_cap_in_range = sum([p.mAh for p in pairs_in_range])
total_edens_in_range = sum([p.mAh * p.voltage for p in pairs_in_range])
return total_edens_in_range / total_cap_in_range
[docs] def get_capacity_grav(self, min_voltage=None, max_voltage=None, use_overall_normalization=True):
"""
Get the gravimetric capacity of the electrode.
Args:
min_voltage (float): The minimum allowable voltage for a given
step.
max_voltage (float): The maximum allowable voltage allowable for a
given step.
use_overall_normalization (booL): If False, normalize by the
discharged state of only the voltage pairs matching the voltage
criteria. if True, use default normalization of the full
electrode path.
Returns:
Gravimetric capacity in mAh/g across the insertion path (a subset
of the path can be chosen by the optional arguments).
"""
pairs_in_range = self._select_in_voltage_range(min_voltage, max_voltage)
normalization_mass = (
self.normalization_mass
if use_overall_normalization or len(pairs_in_range) == 0
else pairs_in_range[-1].mass_discharge
)
return sum([pair.mAh for pair in pairs_in_range]) / normalization_mass
[docs] def get_capacity_vol(self, min_voltage=None, max_voltage=None, use_overall_normalization=True):
"""
Get the volumetric capacity of the electrode.
Args:
min_voltage (float): The minimum allowable voltage for a given
step.
max_voltage (float): The maximum allowable voltage allowable for a
given step.
use_overall_normalization (booL): If False, normalize by the
discharged state of only the voltage pairs matching the voltage
criteria. if True, use default normalization of the full
electrode path.
Returns:
Volumetric capacity in mAh/cc across the insertion path (a subset
of the path can be chosen by the optional arguments)
"""
pairs_in_range = self._select_in_voltage_range(min_voltage, max_voltage)
normalization_vol = (
self.normalization_volume
if use_overall_normalization or len(pairs_in_range) == 0
else pairs_in_range[-1].vol_discharge
)
return sum([pair.mAh for pair in pairs_in_range]) / normalization_vol * 1e24 / N_A
[docs] def get_specific_energy(self, min_voltage=None, max_voltage=None, use_overall_normalization=True):
"""
Returns the specific energy of the battery in mAh/g.
Args:
min_voltage (float): The minimum allowable voltage for a given
step.
max_voltage (float): The maximum allowable voltage allowable for a
given step.
use_overall_normalization (booL): If False, normalize by the
discharged state of only the voltage pairs matching the voltage
criteria. if True, use default normalization of the full
electrode path.
Returns:
Specific energy in Wh/kg across the insertion path (a subset of
the path can be chosen by the optional arguments)
"""
return self.get_capacity_grav(min_voltage, max_voltage, use_overall_normalization) * self.get_average_voltage(
min_voltage, max_voltage
)
[docs] def get_energy_density(self, min_voltage=None, max_voltage=None, use_overall_normalization=True):
"""
Args:
min_voltage (float): The minimum allowable voltage for a given
step.
max_voltage (float): The maximum allowable voltage allowable for a
given step.
use_overall_normalization (booL): If False, normalize by the
discharged state of only the voltage pairs matching the voltage
criteria. if True, use default normalization of the full
electrode path.
Returns:
Energy density in Wh/L across the insertion path (a subset of the
path can be chosen by the optional arguments).
"""
return self.get_capacity_vol(min_voltage, max_voltage, use_overall_normalization) * self.get_average_voltage(
min_voltage, max_voltage
)
def _select_in_voltage_range(self, min_voltage=None, max_voltage=None):
"""
Selects VoltagePairs within a certain voltage range.
Args:
min_voltage (float): The minimum allowable voltage for a given
step.
max_voltage (float): The maximum allowable voltage allowable for a
given step.
Returns:
A list of VoltagePair objects
"""
min_voltage = min_voltage if min_voltage is not None else self.min_voltage
max_voltage = max_voltage if max_voltage is not None else self.max_voltage
return list(filter(lambda p: min_voltage <= p.voltage <= max_voltage, self.voltage_pairs))
[docs] def get_summary_dict(self, print_subelectrodes=True) -> Dict:
"""
Generate a summary dict.
Args:
print_subelectrodes: Also print data on all the possible
subelectrodes.
Returns:
A summary of this electrode"s properties in dict format.
"""
d = {
"average_voltage": self.get_average_voltage(),
"max_voltage": self.max_voltage,
"min_voltage": self.min_voltage,
"max_delta_volume": self.max_delta_volume,
"max_voltage_step": self.max_voltage_step,
"capacity_grav": self.get_capacity_grav(),
"capacity_vol": self.get_capacity_vol(),
"energy_grav": self.get_specific_energy(),
"energy_vol": self.get_energy_density(),
"working_ion": self.working_ion.symbol,
"nsteps": self.num_steps,
"fracA_charge": self.voltage_pairs[0].frac_charge,
"fracA_discharge": self.voltage_pairs[-1].frac_discharge,
"framework_formula": self._framework_formula,
}
if print_subelectrodes:
def f_dict(c):
return c.get_summary_dict(print_subelectrodes=False)
d["adj_pairs"] = list(map(f_dict, self.get_sub_electrodes(adjacent_only=True)))
d["all_pairs"] = list(map(f_dict, self.get_sub_electrodes(adjacent_only=False)))
return d