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@@ -3,6 +3,7 @@ import numpy as np
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import datetime
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import time
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from matplotlib import pyplot as plt
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+import pymannkendall as mk
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from LIB.MIDDLE.CellStateEstimation.Common.V1_0_1 import BatParam
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class SafetyWarning:
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@@ -31,6 +32,7 @@ class SafetyWarning:
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df_res=pd.DataFrame(columns=['start_time', 'end_time', 'product_id', 'code', 'level', 'info','advice'])
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time_now=datetime.datetime.now()
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+ time_now=time_now.strftime('%Y-%m-%d %H:%M:%S')
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time_sp='0000-00-00 00:00:00'
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#参数初始化.......................................
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@@ -41,6 +43,11 @@ class SafetyWarning:
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R2_list=[]
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voltsigmafault_list=[]
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uniformfault_list=[]
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+ mk_trend_list=[]
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+ mk_p_list=[]
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+ mk_z_list=[]
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+ mk_Tau_list=[]
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+ mk_slope_list=[]
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if not self.df_short.empty:
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short_current=self.df_short['short_current']
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@@ -103,34 +110,63 @@ class SafetyWarning:
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#电压变化率及电压离群度.................................................................................
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if not self.OutLineVol_Rate.empty and VoltChange.iloc[-1]['time']*36000>18*3600 and len(VoltChange)>5:
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- volt3sigma=np.array(Volt_3Sigma[volt_column[i]].map(lambda x:eval(x)))
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- volt3sigma_sum=np.sum(volt3sigma<-3)
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- #电压变化率
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VoltChange[volt_column[i]]=VoltChange[volt_column[i]].map(lambda x:eval(x))
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y=VoltChange[volt_column[i]]
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- a1,b1=np.polyfit(VoltChange['time'].tolist(),y.tolist(),1)
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- y1=a1*VoltChange['time']+b1
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- y_mean=y.mean()
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- R2=1-(np.sum((y1-y)**2))/(np.sum((y-y_mean)**2))
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- R2_list.append(R2)
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+ volt3sigma=np.array(Volt_3Sigma[volt_column[i]].map(lambda x:eval(x)))
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+ volt3sigma_sum=np.sum(volt3sigma<-3)
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+ # #电压变化率
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+ # a1,b1=np.polyfit(VoltChange['time'].tolist(),y.tolist(),1)
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+ # y1=a1*VoltChange['time']+b1
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+ # y_mean=y.mean()
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+ # R2=1-(np.sum((y1-y)**2))/(np.sum((y-y_mean)**2))
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+ # R2_list.append(R2)
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- volt_rate.append(a1)
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- # plt.plot(xtime1,y1,label=a1)
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+ # volt_rate.append(a1)
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+ # plt.plot(xtime1,y1,label='单体电压'+str(i+1))
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+ # plt.xlabel('时间', fontsize=25)
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+ # plt.ylabel('SOC差', fontsize=25)
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+ # plt.xticks(fontsize=20)
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+ # plt.yticks(fontsize=20)
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# plt.scatter( xtime1,VoltChange[volt_column[i]],marker='o')
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- # plt.legend(loc='best')
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- # plt.title('单体电压'+str(i+1))
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+ # plt.legend(bbox_to_anchor=(1, 0), loc=3, fontsize=16)
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+ # plt.title(self.celltype)
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# plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
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# plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
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- # plt.show()
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+ # # plt.show()
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if volt3sigma_sum>len(volt3sigma)/2:
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voltsigmafault=1
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else:
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voltsigmafault=0
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+ voltsigmafault_list.append(voltsigmafault)
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+
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+ #mana-kendell趋势检验
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+ mk_res=mk.regional_test(np.array(y))
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+ mk_trend_list.append(mk_res.trend)
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+ mk_p_list.append(mk_res.p)
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+ mk_z_list.append(mk_res.z)
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+ mk_Tau_list.append(mk_res.Tau)
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+ mk_slope_list.append(mk_res.slope)
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+ """
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+ trend：趋势；
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+ h：有无趋势；
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+ p：趋势的显著水平，越小趋势越明显；
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+ z：检验统计量，正代表随时间增大趋势，负代表随时间减小趋势；
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+ Tau：反映两个序列的相关性，接近1的值表示强烈的正相关，接近-1的值表示强烈的负相关；
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+ s：如果S是一个正数，那么后一部分的观测值相比之前的观测值会趋向于变大；如果S是一个负数，那么后一部分的观测值相比之前的观测值会趋向于变小
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+ slope：趋势斜率
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+ """
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+ # print('单体电压{}：\n'.format(i+1), mk_res)
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+
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else:
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volt_rate.append(0)
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R2_list.append(0)
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- voltsigmafault_list.append(voltsigmafault)
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+ voltsigmafault_list.append(0)
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+ mk_trend_list.append(0)
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+ mk_p_list.append(0)
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+ mk_z_list.append(0)
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+ mk_Tau_list.append(0)
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+ mk_slope_list.append(0)
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#电芯SOC排名判断.............................................................................
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if not self.df_uniform.empty:
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@@ -151,13 +187,24 @@ class SafetyWarning:
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faultinfo='电芯{}发生热失控安全预警'.format(i+1)
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faultadvice='1联系用户远离电池，立刻召回电池'
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df_res.loc[0]=[time_now, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
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+ break
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else:
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pass
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- #电池电压变化率热失控预警...............................................................................
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+ #电池电压变化率离群度计算...............................................................................
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volt_rate_std=np.std(volt_rate)
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volt_rate_mean=np.mean(volt_rate)
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volt_rate_3sigma=(np.array(volt_rate)-volt_rate_mean)/volt_rate_std
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+
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+ #mk离群度计算
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+ mk_slope_std=np.std(mk_slope_list)
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+ mk_slope_mean=np.mean(mk_slope_list)
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+ mk_slope_3sigma=(np.array(mk_slope_list)-mk_slope_mean)/mk_slope_std
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+
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+ mk_z_std=np.std(mk_z_list)
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+ mk_z_mean=np.mean(mk_z_list)
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+ mk_z_3sigma=(np.array(mk_z_list)-mk_z_mean)/mk_z_std
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+
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if not self.df_soh.empty and self.celltype<50:
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cellsoh=eval(self.df_soh.loc[0,'cellsoh'])
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cellsoh_std=np.std(cellsoh)
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@@ -165,8 +212,35 @@ class SafetyWarning:
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cellsoh_3sigma=((np.array(cellsoh)-cellsoh_mean)/cellsoh_std)
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else:
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cellsoh_3sigma=[0]*self.param.CellVoltNums
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- for i in range(len(volt_rate)):
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- if volt_rate[i]<self.param.TrwVoltRate and volt_rate_3sigma[i]<-3 and abs(cellsoh_3sigma[i])<2.5 and voltsigmafault_list[i]==1:
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+
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+ #电压/SOC变化率
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+ # for i in range(len(volt_rate)):
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+ # if volt_rate[i]<self.param.TrwVoltRate and volt_rate_3sigma[i]<-3 and abs(cellsoh_3sigma[i])<2.5 and voltsigmafault_list[i]==1:
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+ # faultcode=110
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+ # faultlv=4
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+ # faultinfo='电芯{}发生热失控安全预警'.format(i+1)
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+ # faultadvice='2联系用户远离电池，立刻召回电池'
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+ # df_res.loc[0]=[time_now, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
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+ # else:
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+ # pass
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+
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+ #mana-kendall趋势检测
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+ for i in range(len(mk_p_list)):
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+ #适用动态工况判断
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+ if mk_trend_list[i]=='decreasing' and mk_p_list[i]<self.param.mk_p and mk_z_list[i]<self.param.mk_z and mk_Tau_list[i]<self.param.mk_Tau and mk_slope_3sigma[i]<-3 and mk_slope_list[i]<self.param.mk_slope and abs(cellsoh_3sigma[i])<2.5:
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+ faultcode=110
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+ faultlv=4
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+ faultinfo='电芯{}发生热失控安全预警'.format(i+1)
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+ faultadvice='2联系用户远离电池，立刻召回电池'
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+ df_res.loc[0]=[time_now, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
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+ #适用静态工况判断
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+ elif self.celltype<=50 and mk_trend_list[i]=='decreasing' and mk_p_list[i]<self.param.mk_p and mk_z_list[i]<-6 and (mk_Tau_list[i]<-0.9 or mk_z_3sigma[i]<-3) and mk_slope_3sigma[i]<-3.5 and mk_slope_list[i]<-0.025:
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+ faultcode=110
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+ faultlv=4
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+ faultinfo='电芯{}发生热失控安全预警'.format(i+1)
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+ faultadvice='2联系用户远离电池，立刻召回电池'
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+ df_res.loc[0]=[time_now, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
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+ elif self.celltype>50 and mk_trend_list[i]=='decreasing' and mk_p_list[i]<self.param.mk_p and mk_z_list[i]<-6 and (mk_Tau_list[i]<-0.9 or mk_z_3sigma[i]<-3) and mk_slope_3sigma[i]<-3.5 and mk_slope_list[i]<-0.25:
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faultcode=110
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faultlv=4
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faultinfo='电芯{}发生热失控安全预警'.format(i+1)
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@@ -174,7 +248,6 @@ class SafetyWarning:
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df_res.loc[0]=[time_now, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
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else:
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pass
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-
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- # plt.show()
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+ # plt.show()
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return df_res
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