Package: carculator
carculatorProspective environmental and economic life cycle assessment of vehicles made blazing fast.A fully parameterized Python model developed by theTechnology Assessment groupof thePaul Scherrer Institutto perform life cycle assessments (LCA) of passenger cars and light-duty vehicles.Seethe documentationfor more detail, validation, etc.See ourexamples notebookas well.Table of ContentsBackgroundWhat is Life Cycle AssessmentWhy carculatorInstallUsageAs a Python libraryAs a web appSupportMaintainersContributingLicenseBackgroundWhat is Life Cycle Assessment?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.Whycarculator?carculatorallows to:producelife cycle assessment (LCA)results that include conventional midpoint impact assessment indicators as well cost indicatorscarculatoruses time- and energy scenario-differentiated background inventories for the future, based on outputs of Integrated Asessment ModelREMIND.calculate hot pollutant and noise emissions based on a specified driving cycleproduce error propagation analyzes (i.e., Monte Carlo) while preserving relations between inputs and outputscontrol all the parameters sensitive to the foreground model (i.e., the vehicles) but also to the background model
(i.e., supply of fuel, battery chemistry, etc.)and easily export the vehicle models as inventories to be further imported in theBrightway2LCA framework
or theSimaProLCA software.carculatorintegrates well with theBrightwayLCA framework.carculatorwas built based on work described inUncertain environmental footprint of current and future battery electric vehicles by Cox, et al (2018).Installcarculatoris at an early stage of development and is subject to continuous change and improvement.
Three ways of installingcarculatorare suggested.We recommend the installation onPython 3.7 or above.Installation of the latest version, using condaconda install -c romainsacchi carculatorInstallation of a stable release from Pypipip install carculatorUsageAs a Python libraryCalculate the fuel efficiency (orTank to wheelenergy requirement) in km/L of petrol-equivalent of current SUVs for the driving cycle WLTC 3.4
over 800 Monte Carlo iterations:fromcarculatorimport*importmatplotlib.pyplotaspltcip=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 petroll_powertrains=TtW_energy.powertrain[plt.hist(e,bins=50,alpha=.8,label=e.powertrain.values)foreinTtW_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:fromcarculatorimport*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, seeexamples.As a Web appcarculatorhas agraphical user interfacefor fast comparisons of vehicles.SupportDo not hesitate to contact the development team atcarculator@psi.ch.MaintainersRomain SacchiChris MutelContributingSeecontributing.LicenseBSD-3-Clause. Copyright 2023 Paul Scherrer Institut.