Prospective environmental and economic life cycle assessment of vehicles made blazing fast.
BSD-3-CLAUSE License
carculator
Prospective environmental and economic life cycle assessment of vehicles made blazing fast.
A fully parameterized Python model developed by the Technology Assessment group of the Paul Scherrer Institut to perform life cycle assessments (LCA) of passenger cars and light-duty vehicles.
See the documentation for more detail, validation, etc.
See our examples notebook as well.
Life Cycle Assessment (LCA) is a systematic way of accounting for environmental impacts along the relevant phases of the life of a product or service. Typically, the LCA of a passenger vehicle includes the raw material extraction, the manufacture of the vehicle, its distribution, use and maintenance, as well as its disposal. The compiled inventories of material and energy required along the life cycle of the vehicle is characterized against some impact categories (e.g., climate change).
In the research field of mobility, LCA is widely used to investigate the superiority of a technology over another one.
carculator
?carculator
allows to:
carculator
uses time- and energy scenario-differentiated background inventories for the future, based on outputs of Integrated Asessment Model REMIND.carculator
integrates well with the Brightway LCA framework.
carculator
was built based on work described in Uncertain environmental footprint of current and future battery electric vehicles by Cox, et al (2018).
carculator
is at an early stage of development and is subject to continuous change and improvement.
Three ways of installing carculator
are suggested.
We recommend the installation on Python 3.7 or above.
conda install -c romainsacchi carculator
pip install carculator
Calculate the fuel efficiency (or Tank to wheel
energy requirement) in km/L of petrol-equivalent of current SUVs for the driving cycle WLTC 3.4
over 800 Monte Carlo iterations:
from carculator import *
import matplotlib.pyplot as plt
cip = CarInputParameters()
cip.stochastic(800)
dcts, array = fill_xarray_from_input_parameters(cip)
cm = CarModel(array, cycle='WLTC 3.4')
cm.set_all()
TtW_energy = 1 / (cm.array.sel(size='SUV', year=2020, parameter='TtW energy') / 42000) # assuming 42 MJ/L petrol
l_powertrains = TtW_energy.powertrain
[plt.hist(e, bins=50, alpha=.8, label=e.powertrain.values) for e in TtW_energy]
plt.xlabel('km/L petrol-equivalent')
plt.ylabel('number of iterations')
plt.legend()
Compare the carbon footprint of electric vehicles with that of rechargeable hybrid vehicles for different size categories today and in the future over 500 Monte Carlo iterations:
from carculator import *
cip = CarInputParameters()
cip.stochastic(500)
dcts, array = fill_xarray_from_input_parameters(cip)
cm = CarModel(array, cycle='WLTC')
cm.set_all()
scope = {
'powertrain': ['BEV', 'PHEV'],
}
ic = InventoryCalculation(cm)
results = ic.calculate_impacts()
data_MC = results.sel(impact_category='climate change').sum(axis=3).to_dataframe('climate change')
plt.style.use('seaborn')
data_MC.unstack(level=[0, 1, 2]).boxplot(showfliers=False, figsize=(20, 5))
plt.xticks(rotation=70)
plt.ylabel('kg CO2-eq./vkm')
For more examples, see examples.
carculator
has a graphical user interface for fast comparisons of vehicles.
Do not hesitate to contact the development team at [email protected].
See contributing.
BSD-3-Clause. Copyright 2023 Paul Scherrer Institut.