purpose
既往文献の真似をしつつトラックの燃費とか必要な諸元を求めようという簡単な計算プログラム
再現性の確認がメインかな
code
〜 # -*- coding: utf-8 -*- """ Created on Tue Dec 17 20:03:28 2019 @author: aoki """ #%%import import matplotlib.pyplot as plt import pandas as pd #read test data df = pd.read_excel('WLTP-DHC-12-07e.xls',sheet_name='WLTC_class_3') #t:c8~,v:e8~,a:f8~(m/s/s) mile=df.iat[9,10]/1000#m to km #%%input g=9.8#m/s/s coef_arg=0.95 coef_roll=0.01 rho=1.225 eta_e=0.8 eta_c=0.9 #by outsource #vehicle shape param weight=20#t #payload area=7.1#m2 #coef sigma=0.15 coef_d=0.6#?? #%%cal #initialize i=0 results=[] weight=weight*1000#t to kg SoC0=0.8#no dim battery=(740,3000,4800,6640)#kg #main for i in battery: print('***** battery weight /kg *****',i) #initialize capacity=0 j=0 #listas RL_roll=[] RL_air=[] RL_accel=[] #dataframe to make graph & table Rdf=[] Rdf0=[] Rdfp=[] #%% for j in range(len(df)-7):#as time #vehicle moving param v=df.iat[7+j,4]/3.6#km/h to m/s accel=df.iat[7+j,5]#m/s/s # R_roll=coef_roll*(weight+i)*g#necessary? R_air=rho/2*coef_d*area*v**2 if accel>=0: R_accel=(1+sigma)*(weight+i)*accel else:#Regenerate as negative with speed separete?? R_accel=(1+sigma)*(weight+i)*accel*0.411 R=(R_roll+R_air+R_accel)/coef_arg#N #%%R lists append as kWh to drow graph RL_roll.append(R_roll*v/1000/3600) RL_air.append(R_air*v/1000/3600) RL_accel.append(R_accel*v/1000/3600) #integral capacity+=v*R#W=Nm/s to Ws capacity=capacity/eta_e/eta_c/1000/3600#Ws to kWh: used energy #%%output plt.figure() fig, ax1=plt.subplots() ax1.plot(RL_roll,label='roll') ax1.plot(RL_air,label='air') ax1.plot(RL_accel,label='accel') plt.xlabel('time /sec.') plt.ylabel('Resistance work /kWh') plt.legend() ax2=ax1.twinx() ax2.plot(df.iloc[7:len(df),4],label='velocity',linestyle= 'dashed',color='r') #plt.legend() plt.ylabel('velocity /km/h') plt.show() #%%R work ratio cal #figure plt.figure() Rdf0=pd.Series({'1':sum(RL_roll),'2':sum(RL_air),'3':sum(RL_accel),'4':0.}) plt.pie(Rdf0[0:3],labels=['roll','air','accel (including regenerate)'], autopct="%1.1f%%") title='Resistance work ratio\n'+'when battery weight is '+str(i)+'kg' plt.title(title) plt.show() #table Rdf0[3]=sum(Rdf0) Rdfp=Rdf0/(Rdf0[3])*100 Rdf=pd.DataFrame([Rdf0,Rdfp],index=['total resistance work /kWh','ratio /%']) Rdf.columns=['roll','air','accel','total'] pd.options.display.float_format = '{:.2f}'.format print(Rdf) #%%estimate cf LEAF param econ=mile/capacity/SoC0 est=0.141*i#kWh vol=est/0.114/1000 dist=est/capacity*mile#distance range SoC=100-4*80/dist*100#real SoC ratio as est by 4h*80km/h should be positive price=est/6.11#only battery cf tesla results.append([capacity,econ,dist,est,vol,SoC,price]) #%% print('*****summary*****') results=pd.DataFrame(results,index=battery, columns=['cap. /kWh','econ./km/kWh','dist./km', 'cap.*/kWh','vol.*/m3','SoC* /%','price/¥10^4']) #fig by RL plt.figure() results.plot(subplots=True) plt.xlabel('battery weight /kg') plt.show() plt.figure() results.plot(y='econ./km/kWh') plt.show() plt.figure() results.plot(y='SoC* /%') plt.show() plt.figure() results.plot(x='dist./km',y='econ./km/kWh') plt.show() #%% results=results.transpose() pd.options.display.float_format = '{:.2f}'.format print(results) results.to_csv('mileage_output.csv') 〜
result
