def charge_time(self, step = [0,30,60,120,180,240,300,360,420,480,540,600,660,720,780,840,900,1500]):
[_, time_d, time_d_c, mode, _, _, _]=self.charge_ana()
freq = hist_func_np.hist_con(time_d,step)
re1=pd.DataFrame(freq[1])
re1.index=freq[0]
re1.columns=['频数']
re2 = hist_func_np.box_hist(time_d,'充电时长[min]')
freq = hist_func_np.hist_con(time_d_c,step)
re3=pd.DataFrame(freq[1])
re3.index=freq[0]
re3.columns=['频数']
re4 = hist_func_np.box_hist(time_d_c,'折算满充时长[min]')
[a, b, c] = hist_func_np.hist_con_dis( time_d_c, step, mode,['mode2','mode3_2','mode3_1','DC'],show_hist=1)
re5=pd.DataFrame(np.array(b).T)
re5.index=a
re5.columns=['mode2','mode3_2','mode3_1','DC']
re6=pd.DataFrame(np.array(c).T)
re6.index = ['min','1%percentile','25%percentile','50%percentile','75%percentile','99%percentile','max','mean']
re6.columns = ['mode2','mode3_2','mode3_1','DC']
print(re1, re2, re3, re4, re5, re6)
return re1, re2, re3, re4, re5, re6
def v_mode(self,step=[0,10,20,30,40,50,60,70,80,90,100,110,120,200],sampling=1/6):
sql="SELECT cast(vehiclespeed,'Float32'), operationmode FROM "+ self.tb1 + self.con+ \
" AND cast(vehiclespeed,'Float32')>0 AND toSecond(uploadtime)<"+str(int(sampling*60))
print(sql)
aus=pd.DataFrame(client.execute(sql))
v=np.array(aus[0])
mode=np.array(aus[1])
freq = hist_func_np.hist_con(v,step)
re1 = pd.DataFrame(freq[1])
re1.index=freq[0]
re1.columns=['频数']
re2 = hist_func_np.box_hist(v,'车速[km/h]')
if self.pro_typ=="PHEV":
[a,b,c] = hist_func_np.hist_con_dis(v,step,mode,['EV','PHEV',"FV"],show_hist=1)
re3=pd.DataFrame(np.array(b).T)
re3.index=a
re3.columns=['EV','PHEV',"FV"]
print(re3)
re4=pd.DataFrame(np.array(c).T)
re4.index=['min','1%percentile','25%percentile','50%percentile','75%percentile','99%percentile','max','mean']
re4.columns=['EV','PHEV',"FV"]
print(re4)
return re1,re2, re3, re4
else:
return re1,re2
def E_motor_workingPoint(self,sampling=1/6):
sql="WITH cast(emvolt,'Float32') * cast(emctlcut,'Float32')/1000 as el_pow " \
"SELECT cast(emspeed, 'Float32') as sp, cast(emtq,'Float32') as tq, " \
" multiIf(sp*tq*el_pow==0,0,emstat=='CONSUMING_POWER',sp*tq/9550/el_pow,emstat=='GENERATING_POWER',el_pow/sp/tq*9550,100) " \
" FROM "+self.tb1 + self.con + " AND emstat in ('CONSUMING_POWER','GENERATING_POWER' ) AND toSecond(uploadtime)<"+str(int(sampling*60))
aus=client.execute(sql)
speed,torq,eff=[],[],[]
for value in aus:
if value[0]!=0 and value[1]!=0:
speed.append(value[0])
torq.append(value[1])
if value[2]>0 and value[2]<1:
eff.append(value[2]*100)
l1=len(aus)
l2=len(speed)
l3=len(eff)
print("-------E-Motor Working Point Analysis---------\r\n")
print("原始抓取点数："+str(l1)+"\r\n")
print("去除转矩为零或转速为零之后工作点数："+str(l2)+" 占比："+str(round(l2/l1*100,2))+"%\r\n")
print("效率值在合理范围数："+str(l3)+" 占比："+str(round(l3/l2*100,2))+"%\r\n")
speed,torq,eff=np.array(speed),np.array(torq),np.array(eff)
freq = hist_func_np.hist_con(eff,[0,20,40,50,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100])
re1=pd.DataFrame(freq[1])
re1.index=freq[0]
re1.columns=['频数']
re2 = hist_func_np.box_hist(eff,'电机工作效率[%]')
[a,b,c] = hist_func_np.hist_cros_2con(speed, range(-4000,13000,500), torq, range(-300,400,50))
re3=pd.DataFrame(c)
re3.index=b
re3.columns=a
re3.to_csv('emotor.csv')
re4 = hist_func_np.box_hist(speed,'电机转速[rpm]')
re5 = hist_func_np.box_hist(torq,'电机转矩[Nm]')
return re1, re2, re3, re4, re5
def charge_soc(self,step=range(0,120,10)):
[_, _, _, _,charg_soc, _, _] = self.charge_ana()
soc_s = charg_soc[0]
soc_e = charg_soc[1]
freq = hist_func_np.hist_con(soc_s,step)
re1=pd.DataFrame(freq[1])
freq[0][-1]='100'
re1.index=freq[0]
re1.columns=['频数']
re2 = hist_func_np.box_hist(soc_s,'充电开始SOC[%]')
freq = hist_func_np.hist_con(soc_e,step)
re3=pd.DataFrame(freq[1])
freq[0][-1]='100'
re3.index=freq[0]
re3.columns=['频数']
re4 = hist_func_np.box_hist(soc_e,'充电结束SOC[%]')
print(re1, re2, re3, re4)
return re1, re2, re3, re4
def E_motor_temp(self,step=range(-10,120,5),sampling=1/6):
sql="SELECT cast(emctltemp,'Int32'), cast(emtemp,'Int32') FROM "+self.tb1 + self.con + \
" AND emstat in ('CONSUMING_POWER','GENERATING_POWER' ) AND toSecond(uploadtime)<"+str(int(sampling*60))
aus=pd.DataFrame(client.execute(sql))
temp_motor=np.array(aus[1])
temp_LE=np.array(aus[0])
freq = hist_func_np.hist_con(temp_motor,step)
re3=pd.DataFrame(freq[1])
re3.index=freq[0]
re3.columns=['频数']
re4 = hist_func_np.box_hist(temp_motor,'电机温度[℃]')
freq = hist_func_np.hist_con(temp_LE,step)
re1=pd.DataFrame(freq[1])
re1.index=freq[0]
re1.columns=['频数']
re2 = hist_func_np.box_hist(temp_LE,'LE温度[℃]')
return re1,re2,re3,re4
def daily_mileage(self,step=range(0,500,20)):
sql="WITH cast(accmiles,'Float32') AS mile " \
"SELECT max(mile)-min(mile) FROM " + self.tb1 + self.con+ \
" GROUP BY deviceid,toDate(uploadtime) "
aus=pd.DataFrame(client.execute(sql))
mile=np.array(aus[0])
freq = hist_func_np.hist_con(mile,step)
re1=pd.DataFrame(freq[1])
re1.index=freq[0]
re1.columns=['频数']
re2 = hist_func_np.box_hist(mile,'每日行驶里程[km]')
return re1,re2
def Meb_mileage(date_range):
a = date_range[0] + " 00:00:00"
b = date_range[1] + " 23:59:59"
sql="SELECT deviceid, uploadtime, charg_s_c, socp, d_soc, acc FROM( " \
"SELECT deviceid, uploadtime, runningDifference(charg_s) as charg_s_c, socp, runningDifference(socp) as d_soc, cast(accmiles,'Float32') as acc " \
"FROM ( "\
"SELECT deviceid, uploadtime, if(chargingstatus=='NO_CHARGING',0,1) AS charg_s, " \
"accmiles, if(soc<0,0,soc) AS socp " \
"FROM ods.rtm_details_v2 " \
"WHERE deviceid like 'LSVUB%' AND uploadtime BETWEEN '"+a+"' AND '"+b+"' " \
"ORDER BY deviceid, uploadtime ) ORDER BY deviceid,uploadtime ) " \
"where charg_s_c IN (1,-1) ORDER BY deviceid,uploadtime "
aus = client.execute(sql)
#aus=[0 vin 1 时间 2充电变化标志位 3 soc 4 d_soc 5 mileage ]
df = pd.DataFrame(aus)
#df.to_csv('new.csv')
all_drive = []
count_1 = 0 #基数多少次行驶前数据丢失
count_2 = 0 #计数行驶后数据丢失
count_12 = 0 #行驶前，行驶后均数据丢失
i = 1
while i < len(aus):
if aus[i][0] == aus[i - 1][0] and aus[
i - 1][2] == -1 and aus[i][2] == 1: #i-1时刻是开始行驶，i是结束行驶时刻
start_d_soc = abs(aus[i - 1][4])
end_d_soc = abs(aus[i][4])
if start_d_soc > 5 or end_d_soc > 5:
if start_d_soc > 5:
count_1 += 1
if end_d_soc > 5:
count_2 += 1
if start_d_soc > 5 and end_d_soc > 5:
count_12 += 1
elif aus[i - 1][3] > aus[i][3] and aus[
i - 1][5] < aus[i][5]: # 行驶过程中SOC减少，里程增加
all_drive.append([
aus[i][0], aus[i - 1][1], aus[i][1], aus[i - 1][3],
aus[i][3], aus[i - 1][5], aus[i][5]
])
#all_drive=[0vin,1time_start,2time_end,3soc_start,4soc_end,5mile_start,6mile_end]
i += 2
else:
i += 1
l1 = len(all_drive) + count_1 + count_2 - count_12
soc_r = []
mile_r = []
mile_r_c = []
Energy_consump = []
for i, a in enumerate(all_drive):
soc_r.append(a[3] - a[4])
mile_r.append(a[6] - a[5])
l2 = len(all_drive)
print("------mile perCharging analysis---------\r\n")
print("原始行驶次数：" + str(l1) + "\r\n")
print("充电前数据丢失次数：" + str(count_1) + "占比：" +
str(round(count_1 / l1 * 100, 2)) + "%\r\n")
print("充电后数据丢失次数：" + str(count_2) + "占比：" +
str(round(count_2 / l1 * 100, 2)) + "%\r\n")
print("充电前后数据均丢失次数：" + str(count_12) + "占比：" +
str(round(count_12 / l1 * 100, 2)) + "%\r\n")
print("处理后行驶次数：" + str(l2) + "\r\n")
mile_r = np.array(mile_r)
freq = hist_func_np.hist_con(mile_r, range(0, 800, 50))
re1 = pd.DataFrame(freq[1])
re1.index = freq[0]
re1.columns = ['频数']
re2 = hist_func_np.box_hist(mile_r, '每次充电间隔里程[km]')
charging_energy = 82 #ID4x 82kWh 冲入电量88kWh,来源CLTC充电测试平均值
for a in all_drive:
#all_drive=[0vin,1time_start,2time_end,3soc_start,4soc_end,5mile_start,6mile_end]
if (a[6] - a[5]) > 10 and (a[3] - a[4]) > 5:
Energy_consump.append(charging_energy * (a[3] - a[4]) /
(a[6] - a[5]))
mile_r_c.append((a[6] - a[5]) / (a[3] - a[4]) * 100)
mile_r_c = np.array(mile_r_c)
Energy_consump = np.array(Energy_consump)
freq = hist_func_np.hist_con(mile_r_c, range(0, 800, 50))
re3 = pd.DataFrame(freq[1])
re3.index = freq[0]
re3.columns = ['频数']
re4 = hist_func_np.box_hist(mile_r_c, '折算纯电续驶里程[km]')
freq = hist_func_np.hist_con(Energy_consump,
[0, 13, 14, 15, 16, 17, 18, 19, 20, 50])
re5 = pd.DataFrame(freq[1])
re5.index = freq[0]
re5.columns = ['频数']
re6 = hist_func_np.box_hist(Energy_consump, '折算电耗[kWh/100km]')
return re1, re2, re3, re4, re5, re6
def percharge_mile(self, step=range(0,500,20)):
sql="SELECT deviceid, uploadtime, charg_s_c, socp, d_soc, acc FROM( " \
"SELECT deviceid, uploadtime, runningDifference(charg_s) as charg_s_c, socp, runningDifference(socp) as d_soc, cast(accmiles,'Float32') as acc " \
"FROM (SELECT deviceid, uploadtime, if(chargingstatus=='NO_CHARGING',0,1) AS charg_s, accmiles, if(soc<0,0,soc) AS socp " \
"FROM " + self.tb1 + self.con+ " ORDER BY deviceid, uploadtime ) ORDER BY deviceid,uploadtime ) " \
"where charg_s_c IN (1,-1) ORDER BY deviceid,uploadtime "
aus=client.execute(sql)
#aus=[0 vin 1 时间 2充电变化标志位 3 soc 4 d_soc 5 mileage ]
df=pd.DataFrame(aus)
#df.to_csv('new.csv')
all_drive=[]
count_1=0#基数多少次行驶前数据丢失
count_2=0#计数行驶后数据丢失
count_12=0#行驶前，行驶后均数据丢失
i=1
while i<len(aus):
if aus[i][0]==aus[i-1][0] and aus[i-1][2]==-1 and aus[i][2]==1:#i-1时刻是开始行驶，i是结束行驶时刻
start_d_soc=abs(aus[i-1][4])
end_d_soc=abs(aus[i][4])
if start_d_soc>5 or end_d_soc>5:
if start_d_soc>5:
count_1+=1
if end_d_soc>5:
count_2+=1
if start_d_soc>5 and end_d_soc>5:
count_12+=1
elif aus[i-1][3]>aus[i][3] and aus[i-1][5]<aus[i][5]:# 行驶过程中SOC减少，里程增加
all_drive.append([aus[i][0],aus[i-1][1],aus[i][1],aus[i-1][3],aus[i][3],aus[i-1][5],aus[i][5]])
#all_drive=[0vin,1time_start,2time_end,3soc_start,4soc_end,5mile_start,6mile_end]
i+=2
else:
i+=1
l1=len(all_drive)+count_1+count_2-count_12
soc_r=[]
mile_r=[]
mile_r_c=[]
Energy_consump=[]
for i,a in enumerate(all_drive):
soc_r.append(a[3]-a[4])
mile_r.append(a[6]-a[5])
l2=len(mile_r_c)
print("------mile perCharging analysis---------\r\n")
print("原始行驶次数："+str(l1)+"\r\n")
print("充电前数据丢失次数："+str(count_1)+"占比："+str(round(count_1/l1*100,2))+"%\r\n")
print("充电后数据丢失次数："+str(count_2)+"占比："+str(round(count_2/l1*100,2))+"%\r\n")
print("充电前后数据均丢失次数："+str(count_12)+"占比："+str(round(count_12/l1*100,2))+"%\r\n")
print("处理后行驶次数："+str(l2)+"\r\n")
mile_r=np.array(mile_r)
freq = hist_func_np.hist_con(mile_r,step)
re1=pd.DataFrame(freq[1])
re1.index=freq[0]
re1.columns=['频数']
re2 = hist_func_np.box_hist(mile_r,'每次充电间隔里程[km]')
if self.pro_typ=='BEV':
charging_energy=37 #lavida BEV 实际SOC96~8% 冲入电量37kWh,来源NEDC充电测试平均值
for a in all_drive:
if (a[6]-a[5])>10 and (a[3]-a[4])>5:
Energy_consump.append(charging_energy*(a[3]-a[4])/(a[6]-a[5]))
mile_r_c.append((a[6]-a[5])/(a[3]-a[4])*100)
mile_r_c=np.array(mile_r_c)
Energy_consump = np.array(Energy_consump)
freq = hist_func_np.hist_con(mile_r_c,step)
re3 = pd.DataFrame(freq[1])
re3.index = freq[0]
re3.columns = ['频数']
re4 = hist_func_np.box_hist(mile_r_c,'折算纯电续驶里程[km]')
freq = hist_func_np.hist_con(Energy_consump,np.arange(6,30,0.5))
re5=pd.DataFrame(freq[1])
re5.index=freq[0]
re5.columns=['频数']
re6 = hist_func_np.box_hist(Energy_consump,'折算电耗[kWh/100km]')
return re1, re2, re3, re4, re5, re6
else:
return re1,re2